Scala is a general-purpose programming language that combines object-oriented and functional programming features. It is designed to be concise, expressive, and scalable. Scala is often used for large-scale data processing and machine-learning applications.

Here are some of the key features of Scala:

• Concise: Scala code is typically more concise than Java code. This is due to the use of features such as pattern matching and implicit conversions.
• Expressive: Scala is a very expressive language. This means that it can be used to write code that is both clear and concise.
• Scalable: Scala is a scalable language. This means that it can be used to write code that can be easily scaled up to handle large amounts of data.
• Functional: Scala is a functional language. This means that it supports features such as higher-order functions and immutable data structures.
• Object-oriented: Scala is also an object-oriented language. This means that it supports features such as classes, inheritance, and polymorphism.

What are the top use cases of Scala?

Scala is a general-purpose programming language that is often used for large-scale data processing and machine-learning applications. Here are some of the top use cases of Scala:

• Data processing: Scala is a popular choice for data processing applications due to its conciseness, expressiveness, and scalability. Scala can be used to process large amounts of data quickly and efficiently.
• Machine learning: Scala is a good choice for machine learning applications due to its functional programming features. Scala can be used to write code that is both clear and concise, which is important for machine learning applications.
• Web development: Scala can be used to develop web applications. Scala is a good choice for web development because it is a scalable and expressive language.
• Distributed systems: Scala is a good choice for distributed systems because it is a scalable and concurrent language. Scala can be used to write code that can be easily scaled up to handle large amounts of data.
• Big data: Scala is a good choice for big data applications due to its scalability and expressiveness. Scala can be used to process large amounts of data quickly and efficiently.

What are the features of Scala?

Scala is a feature-rich programming language that combines object-oriented and functional programming paradigms. Its design aims to enhance developer productivity, promote code expressiveness, and provide a versatile platform for building a wide range of applications.

Here are some of the key features of Scala:

• Concise: Scala code is typically more concise than Java code. This is due to the use of features such as pattern matching and implicit conversions.
• Expressive: Scala is a very expressive language. This means that it can be used to write code that is both clear and concise.
• Scalable: Scala is a scalable language. This means that it can be used to write code that can be easily scaled up to handle large amounts of data.
• Functional: Scala is a functional language. This means that it supports features such as higher-order functions and immutable data structures.
• Object-oriented: Scala is also an object-oriented language. This means that it supports features such as classes, inheritance, and polymorphism.

What is the workflow of Scala?

The workflow of Scala is similar to the workflow of other programming languages. Here are the general steps involved in the Scala workflow:

• Write the code: The first step is to write the code in Scala. Scala code is typically written in text files with the .scala extension.
• Compile the code: Once the code is written, it needs to be compiled. The Scala compiler converts the Scala code into a bytecode that can be run by the Java Virtual Machine (JVM).
• Run the code: The compiled code can then be run by the JVM. The JVM will execute the bytecode and produce the desired output.
• Debug the code: If there are any errors in the code, the Scala compiler will report them. The errors can then be debugged and fixed.
• Test the code: Once the code is working correctly, it needs to be tested. Scala code can be tested using a variety of testing frameworks.
• Deploy the code: Once the code is tested and working correctly, it can be deployed to production. Scala code can be deployed to a variety of different environments, including servers, cloud platforms, and mobile devices.

How Scala Works & Architecture?

Scala’s architecture is based on the JVM. The JVM is a platform-independent runtime environment that allows Scala code to be run on a variety of different platforms. Scala’s architecture also includes a number of libraries and frameworks that make it easy to develop Scala applications.

Scala has a number of unique features that make it a powerful and versatile language. These features include:

• Conciseness: Scala code is typically more concise than Java code. This is due to the use of features such as pattern matching and implicit conversions.
• Expressiveness: Scala is a very expressive language. This means that it can be used to write code that is both clear and concise.
• Scalable: Scala is a scalable language. This means that it can be used to write code that can be easily scaled up to handle large amounts of data.
• Functional: Scala is a functional language. This means that it supports features such as higher-order functions and immutable data structures.
• Object-oriented: Scala is also an object-oriented language. This means that it supports features such as classes, inheritance, and polymorphism.

How to Install and Configure Scala?

Here are the steps on how to install and configure Scala:

• 1. Install the Scala compiler: You can download the Scala compiler from the Scala website. Once you have downloaded the Scala compiler, you need to extract the archive to a directory of your choice.
• 2. Configure the Scala environment: You need to configure the Scala environment so that your computer knows where to find the Scala compiler. You can do this by adding the following line to your ~/.bashrc file:

export SCALA_HOME=<path to scala installation>

Replace <path to scala installation> with the path to the directory where you extracted the Scala compiler.

• 3. Verify that Scala is installed correctly: You can verify that Scala is installed correctly by running the following command in your terminal:

scala -version

This command should output the version of Scala that is installed on your computer.

• 4. Install an IDE: There are a number of IDEs that you can use to develop Scala applications. Some popular IDEs for Scala include IntelliJ IDEA, Eclipse, and Visual Studio Code.

• 5. Create a Scala project: Once you have installed an IDE, you can create a Scala project. The steps for creating a Scala project will vary depending on the IDE that you are using.
• 6. Write some Scala code: Once you have created a Scala project, you can start writing some Scala code. Scala code is typically written in text files with the .scala extension.
• 7. Compile and run your Scala code: Once you have written some Scala code, you need to compile and run it. You can do this by running the following command in your terminal:

scalac <path to scala file>

• This command will compile the Scala file and create a bytecode file. You can then run the bytecode file by running the following command:

scala <path to bytecode file>

This command will run the Scala code and produce the desired output.

• 8. Test your Scala code: Once you have written and compiled your Scala code, you need to test it. You can do this by using a testing framework such as ScalaTest or Specs2.

• 9. Deploy your Scala code: Once you have tested your Scala code and it is working correctly, you can deploy it to production. You can deploy Scala code to a variety of different environments, including servers, cloud platforms, and mobile devices.

The Problem: Kubernetes Was Built for Production, Not for ML Experimentation

If your organization is running machine learning workloads, you have almost certainly run into a familiar tension: data science teams need enormous compute resources, and your Kubernetes cluster is right there. The temptation to hand them cluster access is real. The consequences, however, are predictable.

Kubernetes was engineered for running stable, production-grade, replicated services. ML and deep learning (DL) workloads are almost the exact opposite: they are experimental, ephemeral, highly variable in resource demand, and they change constantly. Pretending otherwise creates operational pain for everyone involved.

What Data Scientists Actually Need

Data scientists are researchers first. Their job is to develop models, tune hyperparameters, and run experiments — often hundreds or thousands of them concurrently. To do that effectively, they need:

• Remote compute with GPU access. Local laptops typically lack the memory, storage, and GPU capacity required for modern deep learning workloads.
• Fast environment iteration. Experiment code changes frequently. Keeping Dockerfiles, requirements.txt files, and Conda YAML manifests up to date manually is a distraction from the actual science.
• Domain-specific monitoring. They care about model accuracy, training loss curves, and experiment throughput — not CPU utilization graphs.
• Queue-based job scheduling. Running many concurrent experiments that are short-lived relative to server uptime is an HPC-style challenge, not a standard Kubernetes workload pattern.

Why Giving Data Scientists Raw Kubernetes Access Is a Bad Idea

On a well-managed Kubernetes cluster, a set of operational rules exist for good reason. Granting unrestricted access to ML teams tends to violate most of them:

• Placing all pods in a single namespace.
• Manually managing large numbers of heterogeneous pod types and repository workloads.
• Attempting to control Kubernetes scheduler ordering — which is not reliably possible.
• Leaving thousands of stale, single-use container images on the cluster.
• Triggering uncontrolled node autoscaling in response to a flood of pending experimental jobs.

Unlike traditional software deployment — where a tested, stable artifact is replicated across nodes — ML experimentation means continuously deploying unstable, in-progress code at scale. A cluster of tens of thousands of one-off container images is not a hypothetical; it is a common outcome.

Breaking Down the Real Requirements

The needs of DevOps teams and data science teams differ significantly across every dimension of infrastructure management. Understanding the gap is the first step toward bridging it.

Resource Access

For a DevOps engineer, resource access means namespaces, RBAC policies, network policies, and reliability. For a data scientist, it means: Is this a CPU machine or a GPU machine, and how many GPUs does it have? A simple tiered model — CPU, 1×GPU, 2×GPU, 8×GPU — typically covers the majority of AI team requirements. The nuance that matters to operations is largely irrelevant to the researchers.

Environment Packaging

From a DevOps perspective, environment packaging means containerizing a stable codebase. For ML teams, the development environment changes constantly. Manually maintaining Dockerfiles and dependency manifests is time-consuming and error-prone — stale configurations accumulate quickly because they are decoupled from the actual coding environment. What is needed is automatic extraction of the environment state, with seamless replication from local development machines to remote execution pods.

Monitoring

DevOps monitoring focuses on infrastructure health: CPU usage, memory pressure, disk I/O, pod restarts. Data science monitoring focuses on experiment health: training loss, validation accuracy, throughput, and custom business-relevant metrics. No universal standard exists for ML experiment monitoring, and the ability to define and track custom metrics is often what separates a productive ML team from one flying blind.

Job Scheduling

Kubernetes job scheduling is oriented around resource allocation for long-running or indefinite workloads. ML job scheduling is closer to an HPC queuing problem: there are always more experiments queued than available resources, jobs are relatively short-lived, and the scheduling policy — which job runs first, on which resources, with what priority — directly affects research velocity. Kubernetes was not designed to optimize for this pattern.

MLOps: A Purpose-Built Layer for AI Workloads

MLOps tooling has emerged specifically to address this gap. Rather than replacing Kubernetes, a well-designed MLOps platform sits on top of it — presenting a resource interface tailored to data science teams while letting Kubernetes continue to do what it does well: managing the underlying hardware cluster.

From a Kubernetes perspective, the MLOps platform is just another application: a replicated service with a known resource footprint, deployed and managed like anything else on the cluster. From the data science team’s perspective, it exposes their allocated compute nodes as a logical cluster they control directly.

What an MLOps Platform Provides

• Dedicated job queues with priority ordering, experiment tracking, and isolation from Kubernetes scheduler internals.
• Automatic environment packaging that extracts and replicates the current development environment into a container without requiring the data scientist to manually maintain Dockerfiles.
• Experiment-centric monitoring with customizable metrics dashboards suited to model development workflows.
• Lifecycle management for experiments, including the ability to terminate runaway jobs without DevOps intervention.
• Self-service provisioning so that the AI team can launch, monitor, and clean up their own workloads within the resource budget already allocated by the DevOps team.

The Resulting Division of Responsibility

With an MLOps layer in place, the operational model becomes significantly cleaner:

• DevOps team: Manages Kubernetes, allocates hardware resources to the MLOps platform, and updates resource allocations when needed. They are not in the critical path for every experiment run.
• Data science team: Self-services through the MLOps platform. They launch experiments, track results, manage their queues, and iterate on models without needing to understand Kubernetes internals or file infrastructure tickets.

The MLOps solution acts as the glue: it picks jobs from the ML team’s queues, provisions the appropriate environment (inside a container or as a sidecar), submits the workload to Kubernetes with proper resource requests, monitors execution, and handles cleanup — all without manual DevOps involvement.

Key Takeaways

• Kubernetes is the right tool for managing production infrastructure and hardware clusters. It is not the right tool for managing ML experiment workflows directly.
• Data science teams have fundamentally different requirements for resource access, environment management, monitoring, and scheduling than traditional software engineering teams.
• MLOps platforms address these requirements by providing a purpose-built interface that runs on top of Kubernetes without disrupting its operational model.
• A clean MLOps integration reduces the operational burden on DevOps teams, eliminates cluster sprawl, and gives data scientists the autonomy they need to iterate quickly.

The payoff is straightforward: fewer 2 a.m. provisioning calls, fewer stale container images clogging your registry, and a data science team that can actually focus on building models rather than filing infrastructure tickets.

Data as a Factor of Production

The World Economic Forum has described data as a “new factor of production”—one with the potential to reshape business models, shift industry boundaries, and create entirely new market structures. That framing resonates across industries: whether you work in finance, retail, gaming, or telecoms, the question executives are increasingly asking is not whether to invest in data analytics, but how to make those investments actually work for their teams.

The answer, increasingly, is not simply hiring more data scientists. It is about removing the bottlenecks that prevent every layer of the business—sales, marketing, logistics, operations—from acting on data independently and confidently.

The Bottleneck Problem: Why Most Data Teams Are Stretched Thin

In most organizations today, data access follows a chain of dependency that creates serious throughput problems:

• Business users (in sales, marketing, logistics) need data insights to do their day-to-day jobs—but they rely on data analysts to surface that information.
• Data analysts handle reporting and dashboards, but for predictive or advanced analytics they depend on data scientists.
• Data scientists build models, but they depend on data engineers to ensure that data pipelines, ingestion jobs, and processing infrastructure are running reliably.

This cascade means that a question from a sales manager can take days or weeks to become an answer—filtered through multiple specialists, each with their own queue of work. Restricting advanced analytical tools to a select few is not scalable, and it frustrates every layer of the team.

Democratizing Data Access: What It Actually Means

Democratizing data does not mean giving everyone unrestricted access to raw systems. It means providing role-appropriate interfaces so that each user can independently get the answers they need, without blocking someone else’s work. A well-designed modern analytics platform achieves this through layered access:

• Standard SQL interfaces for data analysts who already know the language—enabling them to write queries, build dashboards, and manage pipelines without waiting on engineers.
• Spreadsheet integrations (such as connected sheet views) for business users who are not comfortable with SQL but are fluent in tools like Excel or Google Sheets.
• Natural language query interfaces that allow non-technical users to ask plain-language questions of their data—no query syntax required.
• Notebook and API integrations (supporting environments like Jupyter, and libraries like Spark, Pandas, and Presto) for data scientists who want to stay in their preferred analytical environments while working against centralized data stores.
• Automated ML capabilities embedded in the SQL layer, so that data analysts can train and evaluate predictive models using familiar syntax—without requiring a dedicated data science handoff.

When data analysts can self-serve, data engineers are freed to focus on higher-value work: data quality, lineage, governance, and architecture. When business users can self-serve, data analysts shift their attention to complex analysis and model validation. Each layer moves up the value chain.

Real-Time and Predictive Analytics: Why They Matter Now

The COVID-19 pandemic demonstrated, more clearly than any business case document, that the ability to react to rapidly shifting conditions is not a luxury—it is a survival capability. Businesses that could observe customer behavior changes in near-real-time and act on predictive signals adapted more effectively than those relying on weekly or monthly reporting cycles.

Streaming analytics pipelines—where data is processed and surfaced as it arrives rather than in scheduled batch jobs—combined with ML-based forecasting give operational teams the situational awareness they need to make decisions in hours rather than days. This is especially relevant in logistics, retail demand planning, fraud detection, and customer service operations.

Practical Steps: Building a Data-Empowered Organization

Moving toward a data-driven operating model is a significant undertaking, but it does not need to happen all at once. A pragmatic sequence typically looks like this:

1. Centralize your data. A single, governed data warehouse or lakehouse that all teams can query eliminates the silos that make cross-functional analytics nearly impossible.
2. Standardize on open interfaces. Avoid proprietary query languages or formats where possible. SQL remains the most widely understood analytical language across data roles.
3. Layer access by role. Deploy the right interface for each user type: SQL for analysts, spreadsheet connectors for business users, notebook APIs for scientists.
4. Introduce AutoML gradually. Embedded ML capabilities (such as building a classification or forecasting model inside a SQL query) allow analysts to experiment with predictive analytics before committing to a full data science engagement.
5. Measure the bottleneck reduction. Track time-to-insight for common business questions before and after tooling changes. This is your primary ROI signal.
6. Evolve governance alongside access. As more users interact with data, invest in data cataloguing, lineage tracking, and role-based access controls to maintain trust and compliance.

The Strategic Payoff: Freeing Your Specialists for High-Value Work

One of the most underappreciated benefits of data democratization is what it does for your most skilled people. When data engineers are no longer spending the majority of their time making data available to analysts—running one-off extracts, fixing broken ad-hoc pipelines—they can concentrate on the architectural work that has lasting impact: building reliable ingestion frameworks, enforcing data quality standards, and designing scalable infrastructure.

Similarly, when data scientists are not bottlenecked on answering routine business questions, they can focus on developing more sophisticated, production-ready models and working on the operationalization (MLOps) challenges that turn a working notebook into a reliable business capability.

The organizations that get this right—where every person who needs data can access it in a form that works for them—are the ones that develop the institutional capacity to innovate continuously, rather than in occasional, expensive bursts.

Key Takeaways for DevOps and Data Engineering Teams

• Data bottlenecks are an organizational problem as much as a tooling problem—address both together.
• Self-service analytics platforms reduce ticket queues and free engineers for strategic infrastructure work.
• Embedding ML capabilities in SQL-based tools lowers the barrier to predictive analytics across data roles.
• Real-time streaming pipelines are increasingly a baseline expectation, not a premium feature.
• Governance and access control must scale alongside democratization—open access without guardrails creates data quality and compliance risk.
• Measure time-to-insight as a concrete engineering KPI to demonstrate the value of platform improvements.

Originally sourced from Forbes.com. The Google Cloud platform examples referenced (BigQuery, BigQuery ML, Connected Sheets, Data QnA) reflect the product landscape as described in the original 2020 article. Verify current product names, features, and availability in the official Google Cloud documentation.

From IT Stack to Data Infrastructure

The concept of the “IT stack” has evolved significantly. Where once it described software running across a mix of hardware resources — tied together by analytics tools, network topology controls, and data channels — organizations now talk about data infrastructure as a first-class architectural concern. Investment in data infrastructure has become a strategic conversation at every level of the business, not just within engineering teams.

It is in this context that Looker, the business intelligence (BI) and analytics platform acquired by and now part of Google Cloud, has shipped a release focused on what the company describes as foundational optimisation. The update targets application builders and developer services specifically, with upgrades spanning the development environment, marketing analytics integrations, and a new data discovery tool.

Looker Blocks for Google Marketing Platform

One of the headline additions is a set of new Looker Blocks for Google Marketing Platform. Looker Blocks are pre-built data models and dashboards designed to accelerate analytics development by providing a reusable, shareable foundation.

These new Blocks are purpose-built for Google Marketing Platform advertising and web data, and include:

• Interactive data exploration capabilities out of the box.
• Light machine learning (ML) predictions baked into the data models, enabling marketers to surface predictive insights without building custom ML pipelines.
• Direct links back to Google Marketing Platform, allowing analysts to act on insights by adjusting campaigns without leaving their analytics workflow.
• Paths for pushing insights back into Google Marketing Platform, enabling a feedback loop between analysis and campaign execution.

Colin Zima, head of Looker analytics and strategy at Google Cloud, has highlighted the ability to “customize data experiences” as central to this release, with deeper Google Cloud integration enabling solutions that were not previously practical.

Redesigned IDE for Model Developers

Looker’s built-in Integrated Development Environment (IDE) — used for authoring LookML data models — has been redesigned to improve developer workflows. Key improvements include:

• More intuitive workflows with advanced controls surfaced in a cleaner interface.
• An object browser that simplifies navigation across a project, making it easier to find, track, and reuse existing LookML objects such as views, explores, and dimensions.
• A single shared repository that can be shared across Looker instances, enabling teams to collaborate on model development without duplicating work.
• Project folders to improve organisation and make collaboration across large LookML projects more manageable.

Together, these changes aim to reduce the friction involved in building and maintaining complex LookML models, particularly in enterprise environments where multiple teams share a single Looker instance.

Developer Data Dictionary

Available through the Looker Marketplace, the new Data Dictionary is an application built using Looker’s extension framework. It provides a dedicated, purpose-built user interface that helps business users locate the fields and metrics they need for analysis — reducing their dependence on developers or data engineers for routine discovery tasks.

From a developer and governance perspective, the Data Dictionary also provides:

• The ability to audit all LookML fields across a project quickly.
• Visualisation of relationships between datasets and explores, helping teams understand data lineage and dependencies.
• Insight into which explores are used most frequently and which are redundant or duplicative — useful for maintaining a lean, well-governed LookML project.

Extension Framework Now Generally Available

Perhaps the most significant long-term development is the general availability of Looker’s extension framework. First announced in November as part of the Looker 7 release, the framework allows developers to build fully custom data experiences within Looker and embed those experiences into existing enterprise applications and workflows.

This positions Looker not just as a standalone BI tool but as a data platform and application development environment. Teams can build bespoke internal tools — dashboards, data explorers, self-service reporting apps — powered by Looker’s semantic layer and embedded directly into the products their users already use.

The Data Dictionary described above is itself an example of an application built on this framework, demonstrating the kind of tooling that becomes possible when analytics infrastructure is treated as a developer platform.

Key Takeaways for DevOps and Data Engineering Teams

• The redesigned LookML IDE reduces development friction for teams managing large or complex data models.
• Looker Blocks for Google Marketing Platform offer a reusable analytics foundation for organisations already invested in the Google advertising ecosystem.
• The Data Dictionary app improves data governance and self-service discovery, reducing analyst dependence on engineering teams.
• The generally available extension framework opens the door to treating Looker as an embedded analytics platform, not just a reporting tool.
• Shared repositories across instances are a meaningful improvement for organisations running multi-instance Looker deployments.

Local Debugging and Broader Desktop Tool Integration

The most significant change in this release is support for writing and running local tests for algorithms using shared local data files. Previously, debugging ML algorithms typically required pushing code to a remote environment, slowing iteration cycles and making root-cause analysis harder. Local debugging removes that friction.

The following desktop and notebook tools can now integrate directly with the Algorithmia enterprise workflow:

• PyCharm – Python IDE widely used by data scientists and engineers
• Jupyter Notebooks – Interactive Python environment standard in data science
• R Shiny – R-based framework for interactive data applications
• Android and iOS SDKs – Enabling on-device or edge ML scenarios
• Cloudinary – Media management platform with ML-powered transformations
• DataRobot – Automated ML platform
• H2O.AI – Open-source ML and AI platform

By meeting data scientists in the tools they already use daily, Algorithmia lowers the barrier to adopting more rigorous DevOps practices—such as unit testing, continuous integration, and peer code review—for ML model development.

Expanded GPU and Cloud Infrastructure Support

The update extends GPU support to the latest instance types available on Amazon Web Services (AWS) and Microsoft Azure. Broader GPU coverage is significant for organizations training or serving computationally intensive deep learning models, where GPU availability and version compatibility directly affect training time and inference latency.

Algorithmia also added support for two specialized AWS environments:

• AWS C2S (Commercial Cloud Services) – A private cloud region operated for U.S. intelligence community workloads
• AWS GovCloud – AWS regions designed to meet U.S. government compliance requirements (ITAR, FedRAMP, etc.)

The addition of C2S and GovCloud support signals Algorithmia’s intent to serve regulated and highly sensitive workloads in the public sector—a market with significant AI investment but stringent data-sovereignty requirements.

Why MLOps and DevOps Convergence Matters

IT teams building AI-powered applications are increasingly applying the same DevOps best practices they use for conventional software: version control, automated testing, CI/CD pipelines, and observability. The challenge is that ML models introduce dimensions traditional DevOps tooling was not designed to handle—data versioning, model drift, feature stores, experiment tracking, and non-deterministic outputs.

Algorithmia CEO Diego Oppenheimer has noted that enabling local desktop debugging is a concrete step toward constructing coherent DevOps workflows around ML. When data scientists can test algorithm behavior locally before promoting code through a pipeline, the feedback loop tightens and defect rates at deployment should decrease.

The Algorithmia platform also provides lineage and provenance tracking across the full ML stack, including:

• Certificate authorities
• Operating system images
• Container images
• Source code and dependencies
• Trained ML models

Full provenance tracking is a prerequisite for auditability and reproducibility—properties that are non-negotiable in regulated industries and increasingly expected in enterprise AI governance frameworks.

The MLOps Gap: Models vs. Application Delivery Velocity

One of the persistent structural tensions in applied AI is the mismatch in release cadence: application code can be deployed continuously, but the ML models embedded in those applications are often updated far less frequently. This gap creates version-skew risks—where the application behavior diverges from what the underlying model was trained to support—and complicates rollback and canary deployment strategies.

Data scientists have historically focused on model accuracy rather than deployment lifecycle. Platforms like Algorithmia attempt to bridge this by giving data science teams guardrails and tooling that match the operational rigor software engineers expect—without forcing data scientists to become DevOps specialists.

Enterprise AI Adoption: Momentum and Risk

At the time of this article (mid-2020), enterprise AI adoption was accelerating, partly driven by cost-reduction pressures following the COVID-19 pandemic. A Deloitte survey of 2,737 IT and line-of-business executives—cited in coverage of this release—found that approximately 61% of respondents believed AI would substantially transform their industry within three years, and roughly 53% reported spending more than $20 million in the prior year on AI technologies and related talent.

However, the same survey found that around 56% of organizations were deliberately slowing AI adoption due to emerging risks—including concerns about model reliability, explainability, regulatory compliance, and security. Improving the local debugging and testing experience addresses some of those reliability concerns directly, by making it easier to catch algorithmic errors before they reach production.

The Road Ahead for Algorithm-Infused Applications

The long-term trajectory is clear: ML and deep learning algorithms will become standard components of most enterprise applications, automating repetitive tasks that currently consume significant engineering and operational time. The immediate challenge is ensuring those algorithms perform consistently as data distributions shift over time—a problem known as model drift—and that deployment workflows are robust enough to detect regressions quickly.

Platforms that successfully unify MLOps and DevOps toolchains reduce the operational overhead of managing this complexity. Algorithmia’s enterprise update is a concrete example of that convergence in practice: bringing local debugging, broader IDE integration, GPU currency, and government-cloud support into a single platform aimed at enterprise AI delivery teams.

Overview

Looker, the business intelligence (BI) and analytics platform that became part of Google Cloud, delivered a major feature release addressing three areas critical to enterprise data teams: modern BI capabilities, foundational performance and infrastructure improvements, and updated tooling for data product development. The release includes support for the complete Google Marketing Platform, significant upgrades for application builders, and a generally available Looker Marketplace where customers can access and share data experiences.

“Looker is continuously evolving to meet the unique and ever growing needs of every business in the way that is best for them. Whether we’re providing multiple choices of cloud providers, the ability to customize data experiences, or the unique solutions coming from our deeper integration with Google Cloud, Looker continues to focus on delivering solutions that have maximum impact on the day-to-day business outcomes of our customers.”

Colin Zima, Head of Looker Analytics and Strategy, Google Cloud

Modern BI Features

Several new and revamped BI features make it easier for business users to extract actionable insights from data without requiring deep technical expertise.

Looker Blocks for Google Marketing Platform

Created in partnership with Google experts, these pre-built analytics blocks are designed to provide a shareable, customizable foundation for analyzing Google Marketing Platform advertising and web data. Key characteristics include:

• Interactive data exploration with light machine learning (ML) predictions embedded in views
• Direct links back into Google Marketing Platform to adjust active campaigns
• Pathways for pushing insights back into the platform
• Fully customizable experiences deployable from Looker Marketplace or the Looker Blocks Directory

The following blocks were made available at release:

• Google Analytics 360
• Campaign Manager + Display & Video 360
• Search Ads 360
• Google Ads Block (refreshed version)

Cross-Filtering in Dashboards

Cross-filtering enables users to slice any metric on one dashboard tile by any dimension surfaced in another tile—with a single click. This feature allows business users to self-serve exploratory analysis directly within a dashboard, surfacing patterns and customer insights that are not immediately apparent from a static view. It reduces dependency on analysts for ad hoc data slicing and supports a more iterative approach to data exploration.

Foundational Performance Improvements

This release included several infrastructure-level enhancements designed to reduce query costs, improve response times, and make better use of existing data infrastructure investments:

• Aggregate awareness: Queries are routed to pre-aggregated tables where possible, reducing compute costs and improving query speed.
• Parallel persistent derived table (PDT) builds: Increases the number of PDTs that can be built simultaneously per connection, reducing wait times in environments with complex data models.
• Faster data downloads: Streaming query results are delivered more quickly when exporting data.
• Pooled database connections: Connection pooling reduces query latency by reusing existing database connections rather than establishing new ones for each query.

Developer and Model Tooling Enhancements

Redesigned Integrated Development Environment (IDE)

The Looker IDE received a significant redesign focused on making LookML development workflows more intuitive. Improvements include:

• More advanced controls for model developers
• An object browser that simplifies navigation and enables reuse of available objects
• Single shared repositories that can be used across multiple Looker instances
• Project folders for improved organization and team collaboration

System Administrator Controls

Administrators in large enterprise deployments gained more granular controls, including:

• Visibility into, and control over, system activity and usage patterns
• Improved user and group management tooling
• The ability to merge Looker-based permissions with corporate identity and access management systems

“Data is essential to our mission of delivering an innovative talent acquisition suite that empowers our customers to grow their companies and help people find jobs. The only way this continues to be sustainable and scalable is because Looker provides tools such as the elite systems activity manager that allows us to manage content, improve adoption, and audit user data over time.”

Zach Linder, VP Machine Learning & Data Analytics, Jobvite

Looker Marketplace and the Extension Framework

Looker Marketplace — Generally Available

Looker Marketplace reached general availability with this release. It serves as a central location where teams can access, deploy, and share data experiences—including all Looker Blocks and community-built custom applications. Teams can rapidly deploy third-party or internally developed applications to their own organization, and optionally share them with the broader Looker customer base. The Marketplace hosts all official Looker Blocks, including the COVID-19 Data Block launched earlier in 2020.

Data Dictionary Application

Available via the Marketplace, the Data Dictionary is a purpose-built application developed using Looker’s extension framework. It provides business users with a dedicated UI to locate the fields and metrics they need for analysis, while giving developers the ability to:

• Audit all LookML fields across a project
• Visualize relationships between datasets and Explores
• Identify the most frequently used data sets and flag duplicate Explores

Extension Framework — Generally Available

Originally announced in November as part of Looker 7, the extension framework reached general availability with this release. It allows developers to build fully customized data experiences within Looker and embed those experiences into existing enterprise applications and workflows—without requiring users to leave their primary tools.

“The National Response Portal was developed to help U.S. healthcare organizations, governments and the public to understand and respond to the impact of COVID-19. Looker’s ability to quickly integrate disparate data sources and rapidly iterate on visualizations was essential in our ability to launch the portal in the midst of a crisis. Looker’s unique extension framework gave us the ability to quickly build and integrate disparate data sources.”

Michael Ames, Sr. Director of Healthcare and Life Sciences, SADA (Google Cloud Premier Partner)

Summary

This August 2020 Looker release represents a broad push across the BI and analytics stack: improved self-service capabilities for business users via cross-filtering and Google Marketing Platform blocks, meaningful infrastructure optimizations for data engineering teams, and a more capable and generally available development ecosystem through the IDE redesign, Marketplace, and extension framework. For teams already invested in Google Cloud, the tighter marketing platform integration provides a direct path from raw advertising data to actionable insights without leaving the Looker environment.

Udacity and Microsoft Launch Azure Machine Learning Scholarship Programme

US-based online learning platform Udacity announced a partnership with Microsoft in June 2020 to provide scholarships for a new Machine Learning Nanodegree programme focused on Microsoft Azure. The initiative was designed to expand the pool of data science professionals equipped to work with Azure cloud services, targeting both beginners and those looking to advance existing skills.

How the Two-Phase Programme Works

The scholarship programme is structured in two sequential phases, with the second phase reserved for top performers from the first.

Phase One: Foundation Course (10,000 Seats)

• Capacity: Up to 10,000 applicants
• Duration: Two months
• Course title: Introduction to Machine Learning on Azure
• Approach: Low-code experience, making it accessible to learners without deep programming backgrounds
• Community support: Participants are supported by a community channel moderated by Udacity
• Goal: Complete the prerequisites required for the full Nanodegree programme

A standout feature of the foundation course is Azure Labs, which lets students work on projects directly inside live Azure environments from within the Udacity classroom — removing the friction of setting up personal Azure accounts to complete hands-on exercises.

Phase Two: Full Nanodegree Scholarship (Top 300 Performers)

• Eligibility: Top 300 performers from Phase One
• Award: Full scholarship to the Microsoft Azure Machine Learning Engineer Nanodegree programme
• Focus: Advanced, in-depth machine learning engineering skills on the Azure platform

Application Window

Applications for the programme opened on June 10, 2020 and were scheduled to close on June 30, 2020. The programme was open to candidates seeking to build or deepen skills in Azure cloud services and machine learning.

What the Partnership Aims to Achieve

Julia White, Corporate Vice President of Azure Marketing at Microsoft, described the intent behind the collaboration:

“Through our collaboration with Udacity to offer low-code and advanced courses on Azure Machine Learning, we hope to expand data science expertise as experienced professionals will truly be invaluable resources to solving business problems.”

— Julia White, Corporate Vice President, Azure Marketing, Microsoft

Udacity CEO Gabriel Dalporto also highlighted the opportunity for professionals looking to grow their skill sets:

“Udacity’s training programme for Azure machine learning presents an amazing opportunity for those looking to further expand their skill set.”

— Gabriel Dalporto, CEO, Udacity

Why This Matters for DevOps and Cloud Engineers

Machine learning is an increasingly critical capability for DevOps and cloud engineering teams. Integrating ML pipelines into CI/CD workflows, automating infrastructure scaling based on predictive models, and working with data science teams on Azure-hosted workloads are all becoming standard expectations. Programmes like this lower the barrier of entry for engineers who want to understand or build ML solutions on Azure without requiring a deep mathematics background, thanks to the low-code emphasis in Phase One.

The Azure Labs feature is particularly relevant for practitioners: working in a real cloud environment — rather than a sandboxed simulation — means the skills and troubleshooting experience gained are directly transferable to production work.

Note: This programme was announced in June 2020. Application deadlines and scholarship availability will have passed. Check Udacity’s website and Microsoft Learn for current Azure Machine Learning training offerings and scholarship opportunities.

Why DevOps Needs AI and ML Now

DevOps has already demonstrated its value: by dissolving silos between development and operations teams, it enables faster, more reliable software delivery through continuous integration (CI) and continuous delivery (CD). Organizations that have adopted DevOps consistently report shorter time-to-market cycles and more predictable release cadences.

The challenge is that modern software systems are generating data at a scale and speed that human engineers cannot realistically process alone. Microservices architectures, IoT endpoints, edge computing nodes, and cloud-native platforms each produce torrents of telemetry, logs, and event data. Making sense of that data — in real time, across distributed systems — is precisely the problem that AI and ML are positioned to solve.

Incorporating AI and ML into a DevOps strategy does not replace engineers; it amplifies what they can do. It shifts the focus from reactive firefighting to proactive, data-informed decision-making.

Key Areas Where AI and ML Add Value in DevOps

1. Intelligent Observability and Root-Cause Analysis

Traditional monitoring alerts engineers when something goes wrong. AI-assisted observability goes further: it correlates signals across multiple data sources, surfaces the sequence of events leading up to a failure, and in some cases predicts failures before they affect users. This moves teams from pure diagnostics — understanding what broke — toward prognostics — anticipating what is likely to break and why.

The practical outcome is a measurable reduction in mean time to resolution (MTTR), which directly improves system availability and end-user experience.

2. Automated Testing and Quality Gates

ML models trained on historical test results can prioritize which tests to run first for a given code change, identify flaky tests, and flag code paths that have historically been associated with production incidents. This makes CI pipelines faster without sacrificing confidence in release quality.

3. Release Optimization and Deployment Decisions

AI can analyze deployment history, infrastructure metrics, and real-time traffic patterns to recommend optimal deployment windows, flag risky release candidates, and automate rollback decisions. When a canary deployment begins showing elevated error rates, an ML-driven system can halt or reverse the rollout without waiting for a human to act.

4. Capacity Planning and Cost Management

ML-based forecasting can model future resource demand based on usage trends, seasonal patterns, and planned feature releases. This allows platform and SRE teams to right-size infrastructure proactively rather than reactively scaling in response to incidents.

Security Takes Center Stage: The Rise of DevSecOps

A DevOps pipeline that delivers software fast but insecurely is not delivering business value — it is accumulating risk. DevSecOps addresses this by integrating security controls, testing, and compliance checks directly into the CI/CD pipeline, making security a continuous concern rather than a final checkpoint.

The core premise is straightforward: every member of the team — developers, operations engineers, QA, and security specialists — shares responsibility for the security posture of what they ship. In practice, this means:

• Static application security testing (SAST) and software composition analysis (SCA) running automatically on every pull request.
• Infrastructure-as-code (IaC) scanning catching misconfigured cloud resources before they reach production.
• Secrets detection preventing credentials from being committed to source control.
• Runtime threat detection monitoring deployed workloads for anomalous behavior.

AI and ML make each of these layers more effective. ML models can identify anomalous network behavior, detect novel attack patterns that signature-based tools would miss, and triage vulnerability alerts by severity and exploitability — reducing alert fatigue and helping security teams focus on what matters most.

Automation also reduces human error in security configuration. Misconfigurations — overly permissive IAM policies, unencrypted storage buckets, open security groups — are among the most common root causes of cloud breaches. Automated policy enforcement and continuous compliance scanning catch these issues at the point of creation rather than after the fact.

Moving from Diagnostics to Prognostics: Self-Healing Systems

One of the most compelling — and still largely aspirational — promises of AI-powered DevOps is the self-healing system: infrastructure and applications that detect degradation and remediate it automatically, without human intervention.

Partial versions of this already exist. Kubernetes will restart crashed containers, reschedule pods from failed nodes, and scale deployments based on CPU or custom metrics. Cloud auto-scaling groups replace unhealthy instances. Circuit breakers in service meshes prevent cascading failures. These are rule-based automations, not AI in the strong sense, but they represent the foundation on which more intelligent self-remediation can be built.

True AI-driven remediation — where a system understands context, infers the probable cause of an issue, selects among remediation strategies, executes a fix, and validates the outcome — remains an active area of research and product development. Engineering teams should evaluate vendor claims in this space carefully and look for evidence of real-world production deployments rather than demo-environment results.

Real-Time Insights and Continuous Feedback Loops

A mature AI-augmented DevSecOps pipeline closes the feedback loop between production behavior and development decisions. Real-time operational data — error rates, latency distributions, user engagement signals, security events — flows back into the development process to inform what gets built next and how it gets built.

This creates a continuous learning cycle:

1. Build and deploy — code ships through an automated CI/CD pipeline with integrated security and quality gates.
2. Observe — AI-assisted monitoring surfaces anomalies, performance regressions, and security events.
3. Analyze — ML models correlate signals, identify root causes, and surface actionable recommendations.
4. Act — automated remediation handles routine issues; humans handle complex or novel situations with AI-generated context.
5. Learn — outcomes feed back into models and processes, improving future predictions and automations.

Practical Considerations Before Adopting AI in Your DevOps Pipeline

Introducing AI and ML into a DevOps practice is not a switch you flip. Several conditions need to be in place for it to deliver value:

• Data quality and volume: ML models require sufficient, well-labeled historical data. If your observability stack is incomplete or your data is poorly structured, AI tooling will underperform.
• Cultural readiness: AI-generated recommendations are only useful if engineers trust and act on them. This requires transparency in how models make decisions and a culture that values experimentation.
• Toolchain integration: AI features need to integrate cleanly with existing CI/CD, monitoring, and security tools. Evaluate whether a given solution fits your stack or requires significant retooling.
• Incremental adoption: Start with well-understood problems — anomaly detection, test prioritization, vulnerability triage — before attempting more complex use cases like autonomous remediation.

Summary

AI and machine learning are not replacing DevOps engineers — they are extending what those engineers can accomplish. By automating the analysis of large, complex data sets, surfacing insights faster than human teams can process them manually, and embedding intelligence into security and quality workflows, AI-powered DevSecOps enables organizations to deliver software that is faster, more reliable, and more secure.

The organizations that will benefit most are those that treat AI adoption as an engineering discipline: starting with solid data foundations, integrating incrementally, measuring outcomes rigorously, and maintaining human oversight where the stakes are highest. The technology is maturing rapidly, but thoughtful implementation remains the differentiator.

This article draws on themes originally published on devops.com.

How many defects has it found? Never mind that, check out the architecture flow chart

Altran – in association with Microsoft – has pushed out an open source project to find code defects via AI whenever you commit code.

It is easy to see why Microsoft is keen. This is a self-managed service where you download the code and purchase Azure resources on which to host it. The project from Altran – a consulting and software engineering firm that’s part of Capgemini – uses two Azure Web Apps (one for the UI and one for the API); an Azure virtual network, VPN gateway and virtual network security group; Azure container registry; Azure MariaDB database service; and a container for scheduling the prediction code. Not to mention, it also includes Azure Application Insights for that all-important traffic analysis.

The UI web app is to be configured with two P1v2 Linux plans, each setting you back around £61 per month, and the AI web app with two P2V2 Linux plans at around £123.00 each per month. The MariaDB database is 4 vCores at £221 per month – so you are paying £600 per month before adding in storage costs, virtual network gateway and so on. The code and installation instructions are on GitHub here. The guide explains everything you need to know about deploying the application, but nothing much about how to use it.

The way the system appears to work is that a bug detection model is trained by inspecting open source GitHub projects, including the history of reported bugs in the codebase. Then, say the docs rather vaguely, “Once we have a model that has acceptable value of precision and recall, selected model is deployed for prediction on new commits.” Code that seems likely to be buggy is flagged for further investigation and testing.

According to Altran, “Code Defect AI relies on various ML techniques including random decision forests, support vector machines, multilayer perceptron (MLP) and logistic regression. Historical data is extracted, pre-processed and labelled to train the algorithm and curate a reliable decision model. Developers are given a confidence score that predicts whether the code is compliant or presents the risk of containing bugs.”

Code Defect AI supports integration with third-party analysis tools and can itself help identify bugs in a given program code. Additionally, the Code Defect AI tool allows developers to assess which features in the code have higher weightage in terms of bug prediction, i.e., if there are two features in the software that play a role in the assessment of a probable bug, which feature will take precedence.

The “solution” page looks glossy but The Reg noticed a surprising omission in the presentation. Normally such things have enthusiastic testimonies from happy users. We did find a video in which Altran VP Software Engineering and Cybersecurity Khalid Sebti said that “customers who have used our code defect solution predictions are able to identify over 20 per cent of the defects before the testing process begins.”

But how?

Where is the evidence, though? We asked Altran, which first sent us a document entitled “PowerPoint Presentation” assuring us that this was “an AI powered solution that identified bugs” but with no data to back up the claim. We asked again and were referred to the code on GitHub and in particular the “flowchart of the architecture.” There are several such flowcharts, one which has “storage and data lake Azure” stuck on the side for good measure. We did learn from the aforementioned video that this is merely phase one of the project. In phase two, “the solution expands to other Microsoft AI/ML technologies including ML Server and SQL Server.” The flow chart for this includes the magic word Kubernetes for extra goodness, as well as both SQL Server and Cosmos DB.

Phase two of the project is expanded to use increasing numbers of Azure services
Phase two of the project is expanded to use increasing numbers of Azure services

The question perhaps is whether it makes sense to train your own AI model on defects found in open source GitHub projects when it might be better to subscribe to a service that has done its own model training at scale. On its security blog, Microsoft posted last month that “since 2001 Microsoft has collected 13 million work items and bugs. We used that data to develop a process and machine learning model that correctly distinguishes between security and non-security bugs 99 per cent of the time and accurately identifies the critical, high priority security bugs 97 per cent of the time.”

At Microsoft, the post explained, “47,000 developers generate nearly 30 thousand bugs a month,” which strikes us as good going. The methodology for this is also promised to be open-sourced on GitHub “in the coming months” though it does not appear to be related to the Altran project.

AWS trained its CodeGuru profiler on “hundreds of thousands of internal projects, as well as over 10,000 open source projects in GitHub.” This is the kind of scale which, like Microsoft’s security project, may yield interesting results.

Is this an open source project hoping to attract the support of a sufficient collaboration community to sustain momentum? It looks unlikely; some code has been dumped in GitHub but there is no evidence yet of any community around it. Think of it more as a sample showing how you can code-host an AI project on Azure. In that respect it may be useful, but the “better code faster with Azure AI,” that the PowerPoint promises? We are sceptical but await customer reports with interest.

This article examines the most significant advantages and drawbacks of bringing AI and ML into a DevOps practice, drawing on perspectives from practitioners and technology leaders.

The Benefits of AI and ML in DevOps

Making Sense of Complex Data

Modern software systems generate enormous volumes of logs, metrics, traces, and events. Engineers cannot manually review all of it at the pace that continuous delivery demands. AI and ML models can be trained to categorise and contextualise this data far more quickly than humans, surfacing meaningful signals from the noise.

Kevin Dallas, CEO of Wind River, has argued that AI and ML will become mandatory for analysing and processing operational data at scale. As he explained, the goal is not just to detect that a fault occurred, but to understand the sequence of events leading up to it—giving developers richer context for root-cause analysis, diagnostics, and business-level insights alike.

Earlier Defect Detection and Smarter Code Analysis

A core DevOps objective is finding and fixing defects as early in the pipeline as possible, before they propagate downstream to production. AI-assisted code analysis tools and automated code-review systems can identify severe issues earlier than traditional static analysis, and ML-powered testing tools can highlight coverage gaps and predict where defects are most likely to appear.

Joao Neto, Head of Quality and Acceleration at OutSystems, notes that AI and ML can also analyse value streams to identify bottlenecks and inefficiencies, and can even propose corrective actions. The same principles extend beyond code quality into security, architecture, performance, and user experience—any area where pattern recognition in data can guide better decisions.

Democratising Experimentation

Rigorous A/B testing and experimentation traditionally require specialised skills—data analysts who understand statistical significance, control group design, and result interpretation. Most software teams do not have this expertise on hand, which limits their ability to run meaningful experiments.

ML can lower this barrier by automating much of the statistical heavy lifting, making experimentation accessible to a broader range of team members, and potentially even to business stakeholders. When combined with observability tooling, ML techniques can also help teams understand how customers are actually using a product, what friction points they encounter, and what conditions reliably lead to system failures.

The Risks and Drawbacks

Deployment of AI Assets Is Harder Than It Looks

Many AI initiatives stall not at the modelling stage but at deployment. Building a predictive model in a notebook environment is relatively straightforward; operationalising it reliably within a production pipeline is a different problem entirely.

Peter van der Putten, Director of AI Systems at Pegasystems, identifies several requirements that are specific to deploying AI assets and are not fully addressed by standard DevOps practices. These include:

• Ethical bias checks: Models must be evaluated for discriminatory outputs before they are promoted to production.
• Transparency and explainability requirements: Depending on the use case, a model may need to meet minimum standards of explainability before deployment is appropriate.
• Model drift monitoring: Unlike traditional software, ML models can degrade silently over time as the data they receive diverges from their training distribution.

Standard CI/CD practices—automated testing, integration pipelines, staged rollouts—help, but they are not sufficient on their own. Teams deploying AI assets need to extend their pipelines with ML-specific quality gates.

Narrow AI Can Distract from Deeper DevOps Work

A significant and often overlooked risk is the opportunity cost of investing heavily in AI tooling before the underlying organisational and process foundations are sound.

Nigel Kersten, Field CTO of Puppet, draws a useful distinction: what most DevOps teams are working with is not general-purpose AI that replicates human reasoning, but narrow, task-specific statistical models. This is genuinely useful, but it can become a distraction if leaders treat it as a shortcut to avoid harder organisational work.

“I see far too many enterprise leaders looking to ‘AI’ and Robotic Process Automation as a way of dealing with the complexity and fragility of their IT environments instead of doing the work of applying systems thinking, streamlining processes, creating autonomous teams, adopting agile and lean methodologies, and creating an environment of incremental progress and continuous improvement.”

Nigel Kersten, Field CTO, Puppet

Kersten’s recommended order of operations: first, maximise the value of human expertise by fixing processes and culture. Then, apply ML selectively to augment those humans—automating well-understood, repetitive tasks such as anomaly detection in narrow, defined domains.

Bias and Model Quality Risks

AI and ML models are only as good as the data and processes used to train them. A model trained on historical data that reflects past biases or gaps in coverage will reproduce those problems at scale—and often invisibly. In a DevOps context, this could manifest as anomaly detection systems that miss novel failure modes, or automated code-review tools that flag issues inconsistently across different codebases.

Getting staff to trust and work effectively alongside AI tooling adds another layer of complexity. Without adequate training, documentation, and a culture that encourages engineers to challenge model outputs, AI tools can generate false confidence rather than genuine improvement.

Key Takeaways for DevOps Teams

• Start with clear use cases. AI and ML deliver most value in well-scoped, data-rich domains: log analysis, anomaly detection, test prioritisation, and value-stream analytics. Avoid broad, vague mandates to “add AI” without a defined problem to solve.
• Extend your pipelines for ML assets. Treat models as first-class software artifacts. Add bias checks, explainability validation, and drift monitoring to your CI/CD pipelines alongside standard unit and integration tests.
• Fix the fundamentals first. If your processes are fragile, your teams lack autonomy, or your observability is poor, addressing those issues will deliver more value than any AI tooling overlay.
• Plan for human oversight. AI and ML should augment engineers, not replace their judgement. Design workflows that keep humans informed and in control of consequential decisions.
• Invest in training and buy-in. Teams that understand how a model works—and what its limitations are—will use it more effectively and catch its failures earlier.

The Outlook

As AI and ML capabilities mature, their integration with DevOps practices is likely to deepen. Real-time optimisation of running systems—where models trained on live development, validation, and operational data continuously adjust system behaviour—represents a compelling direction. However, that future depends on teams building sound data infrastructure, responsible deployment practices, and a culture that treats AI as one tool among many rather than a silver bullet.

The teams that will benefit most are those that apply AI and ML thoughtfully, with clear metrics for success, strong human oversight, and a willingness to fix the organisational problems that no model can automate away.

If your test case is causing more harm than good, is it truly useful? In the days of legacy software delivery, with long lead times and great difficulty changing the product once shipped, nearly all test cases (automated or not) were good test cases.

In this era of continuous delivery, though, this calculus has shifted. It’s shockingly easy to end up with a test that inadvertently causes your software to be less stable—whether that’s by building false confidence in the code, by removing trust from the tests themselves, or by taking up so much time that the tests aren’t run frequently enough.

Whether you’re doing automated or manual testing, it’s critical that any software checks that you make to validate your assumptions about code follow three key principles to ensure they are fully compatible with a continuous integration and delivery system.

[ Learn what your team needs to know to start taking advantage of test automation with TechBeacon’s Guide. Plus: Get the Buyer’s Guide For Software Test Automation Tools ]

1. Reliability
Any test case that you’re going to run with any frequency must be reliable; that is, the test case cannot be flaky. Consider an automated check: In a continuous integration environment, this test case could run dozens or hundreds of times a day for a single team.

If a test is only 99% reliable (one false report in every 100 test executions), and you run it 200 times a day, then your team will be investigating false-positive failures at least twice daily. Multiply that by a unit test suite that can have tens of thousands of test cases, and the math becomes clear.

Any test case that is not at least 99.9% reliable should be removed until it can be brought above the reliability threshold.

But what does reliability look like? A test case must take every precaution to avoid a false negative or false positive. It must be repeatable without outside human intervention; it must clean up after itself.

In a fully automated system, there generally isn’t time for a human to, for example, drop tables on the SQL database every few test runs. Even a manually run test case must clean up after itself, because it is an unmanageable mental burden on the test executor to have a continuously shifting starting state.

Why is reliability so important? When developers must routinely waste time investigating false positives or negatives, they quickly lose faith in the automation solution and are likely to ignore real failures alongside the false ones.

2. Importance
In a continuous integration system, the most precious resource you have to spend is engineers’ time. Engineers have grown to expect results quickly, and they are unwilling to wait for something they perceive as wasting time. So ensure that you’re getting relevant results back as quickly as possible.

For example, there’s no point in attempting to run unit tests on code that doesn’t compile. And there’s no point in running an API-level integration test suite if the unit tests on an underlying package don’t pass. You’re assured that the code under test will have to change, so why waste time on a test run that is guaranteed to be thrown away?

Test pyramid
Figure 1. In this modified testing pyramid, unit tests make up the foundation of your testing strategy, integration tests validate across boundaries, and specialty tests at the top capture any slow or complex testing. Source: Melissa Benua.

Always run the most important test cases as quickly as possible, and always run your fastest tests first. Those are nearly always your unit tests; a typical unit test executes in microseconds and can generally be run in parallel. In my continuous integration systems, I can usually process through tens of thousands of unit tests in around 90 seconds.

An integration test is a test that crosses boundaries, usually including at least an HTTP or other machine-to-machine boundary. By definition, these test cases execute in milliseconds and are several orders of magnitude slower than are unit tests.

Finally, a specialty test is anything that’s significantly slower than an integration test (such as an end-to-end automated UI test) or one that requires human intervention or interpretation that slows down the overall reporting of results.

While tests slower than a unit test certainly have value, and absolutely have a place in a continuous integration system, that place is after the faster and more reliable tests have run.

[ Understand quality-driven development with best practices from QA practitioners in TechBeacon’s Guide. Plus: Download the World Quality Report 2019-20 ]

3. Specificity

A good test case should individually do as little as possible to produce a pass/fail outcome as quickly as possible. If you had infinite time in which to execute a test run, overlapping coverage and redundant tests wouldn’t be very important.

But if you only have a budget of five minutes for an entire integration test pass, for example, and every integration test case takes 10 milliseconds, then you have time to run only 30,000 test cases. And if you are doing a UI-based test that is closer to 1 second per test, then you have time for only 300 test cases.

Once you acknowledge the reality of an upper bound to the number of test cases you may run, you can consider exactly how to spend those resources.

Unit test explorer

Figure 2. Always name test cases in a clear, descriptive manner. As you can see above, that makes it easier to diagnose failures. Source: Melissa Benua.

Every test case should be a clear answer to a clear question, the combination of which adds up to a test suite that will give a clear answer about the full set of functionalities under test. Clearly named, atomic test cases should make it easy to locate the potential cause of a test failure, and also make it easy to understand at a glance what has and what has not been tested.

When test cases are clear and atomic, it becomes easy to find coverage overlaps, and thus candidates for deletion.

Now put those principles into practice
Test cases that have many complicated steps and validations are prone to failure (violating Principle 1) and to having a long runtime (violating Principle 2). Consider the following test case:

Ensure Safe Search=Strict Works

Create a new user
Log that user into the UI
Navigate to the user setting page
Change Safe Search setting to Strict
Navigate to the search page
Search for adult content
See that no results are returned
By running through everything end to end, you are inadvertently testing a lot of functionality in order to answer the question the test case is actually posing. Rather than running through the entire experience exactly as an end user would, this test case would be better served if you decompose it into several different cases—and several different suites:

User creation suite
Search suite; our test case goes here
Most likely all of A, B, and C will have a combination of 100x of unit tests and 10x of integration tests, depending on their specific system architecture boundaries. While C may require a logged in user, the purpose of the suite is not to ensure that you can create a user or update the user’s settings.

Creation and setting changes are incidental functionalities that are likely called during test suite setup; if they fail then, do not attempt to test any additional functionalities. Given this known order of precedence, you also want to ensure that test suites are run in A, B, C order, since you know that C is dependent on functionality in A and B. It’s useless to try to execute C if either A or B is known to not work.

Now apply the principles to improve stability
If as you’ve moved to continuous delivery your automated or manual tests are causing your software to be less stable, the steps above are for you. Follow these three key principles and your tests will always be compatible with your organization’s continuous delivery efforts.

Closing that gap is not just a technical problem. It is an organisational and cultural challenge that, when solved well, produces faster delivery, lower risk, and better collaboration across the entire Salesforce team.

Why Salesforce DevOps Has Become Unavoidable

Salesforce has deliberately lowered the barrier to building business solutions. Its declarative, point-and-click tooling means that business administrators — often called “no-code admins” — can iterate and ship new features without writing a single line of code. This speed is genuinely valuable, but it creates a problem at scale: changes made directly in a production org bypass the safety nets that mature engineering teams rely on.

DevOps provides those safety nets. At its core, DevOps is a set of practices, attitudes, and tools that govern how changes move from development through testing and into production. It reduces friction and waste by making that journey repeatable, auditable, and automated. Because Salesforce instances now underpin critical business operations — CRM, sales pipelines, customer service workflows — the consequences of uncontrolled change can range from data corruption to genuine downtime.

The result is that DevOps has become the operating model of choice for teams managing Salesforce at scale. Continuous integration pipelines, automated test suites, and version-controlled metadata are no longer exotic practices reserved for software engineering shops; they are becoming standard expectations for any serious Salesforce delivery team.

The Core Friction: Admins and Version Control

Most DevOps toolchains centre on version control — typically Git — as the single source of truth for all changes. Developers are already comfortable with this model. They commit code, open pull requests, run automated tests, and deploy through a pipeline. For them, this is routine.

Admins, by contrast, have historically worked directly in the Salesforce UI. They click, configure, and save — and their changes take effect immediately in whatever org they are working in. Salesforce does not provide a native mechanism for tracking these configuration changes in version control, which means that without deliberate process design, admin work exists outside the delivery pipeline entirely.

This creates real risks:

• Configuration changes made in production cannot be easily audited or rolled back.
• Developer and admin changes can conflict silently, with neither party aware until something breaks.
• Automated testing cannot protect admin-driven changes that were never committed to the pipeline.

Demystifying Code for Salesforce Admins

One barrier to admin participation in DevOps toolchains is psychological: code feels foreign and intimidating to people who have never needed it. Popular culture does not help — the archetype of the solitary, superhuman hacker makes programming seem like an exclusive skill that takes years to acquire.

The reality is more prosaic. Writing code is, fundamentally, writing — in a language with a stricter grammar than English, but one where the computer provides immediate feedback when you get the syntax wrong. That feedback loop is actually an advantage: you do not have to wait days or weeks to know whether you have made a mistake.

Experienced developers switching to a new language go through exactly the same learning curve that admins face. They work through “Hello World” examples, they copy and adapt snippets, and they make errors repeatedly before developing fluency. The difference is not innate ability — it is familiarity and confidence built through practice.

For admins, the practical goal is not to become a full-stack developer. It is to reach a baseline of code literacy sufficient to:

• Run command-line tools such as the Salesforce CLI to retrieve and deploy metadata.
• Commit changes to a Git repository and understand basic branch workflows.
• Read a pull request diff to understand what changed and why.
• Contribute to or review automated test specifications.

Even a small amount of hands-on experience with the command line tends to break the psychological barrier. Once an admin has successfully run a sf project retrieve start command and seen their org metadata appear as files in a repository, the abstraction becomes concrete and far less intimidating.

What Version Control Actually Gives Admins

Once an admin is comfortable tracking configuration changes in version control, the benefits are immediate and practical:

Full Change History

Every commit records what changed, when, and who made the change. This audit trail is invaluable when debugging a regression or demonstrating compliance. Without version control, answering “what changed in the last two weeks?” often requires manually comparing org snapshots or relying on the Salesforce Setup Audit Trail, which has limited depth and scope.

Rollback Capability

If a configuration change causes a problem, version control makes it straightforward to identify the offending commit and revert it — either by reverting in Git and redeploying, or by comparing the previous and current states of the metadata to understand what needs to change back.

Participation in the Delivery Pipeline

This is perhaps the most significant benefit. Once admin changes are tracked in version control alongside developer changes, a continuous integration (CI) engine can automatically deploy those changes to a scratch org or sandbox, run automated tests against them, and only promote them to production when the tests pass. Admins stop being a risk vector in the pipeline and become an equal contributor to it.

Shared Ownership of Automated Tests

A CI/CD pipeline for Salesforce typically runs Apex unit tests and, increasingly, UI-level tests using frameworks such as Selenium or UTAM. The value of those tests depends entirely on whether they cover the real behaviour of the org — including behaviour driven by admin configuration, not just developer code.

Admins are uniquely positioned to define what correct behaviour looks like for the features they own. When admins can articulate acceptance criteria — or better, contribute directly to writing or reviewing automated tests — they extend the protective coverage of the test suite to their own work. Those tests then guard against future regressions, regardless of whether the regression is introduced by a developer or another admin.

This is a meaningful shift in mindset: from “I made a change” to “I made a change that is now protected by an automated check that will catch any future breakage.” That shift turns testing from a developer concern into a shared team responsibility.

The Organisational Case for Unified DevOps

Keeping admins and developers in separate processes is not a neutral choice — it carries ongoing costs:

• Production risk: Changes made directly in production orgs are effectively uncontrolled experiments that put live data, users, and business operations at risk every time they are made.
• Duplicated effort: When admins and developers are not working from the same baseline, both parties may unknowingly make conflicting changes to the same metadata, requiring time-consuming reconciliation.
• Reduced velocity: Teams that cannot trust their deployment process move slowly and defensively. A shared, automated pipeline with good test coverage allows teams to ship more confidently and more often.
• Governance gaps: Without a shared pipeline, it is difficult to enforce change management controls or demonstrate an auditable process to stakeholders and compliance teams.

Bringing admins into the delivery pipeline is an investment in both technical resilience and cross-team collaboration. It gives admins a structural reason to communicate with developers about timing, dependencies, and testing. It gives developers visibility into configuration changes that would otherwise be invisible to them. And it gives the organisation a single, governed pathway through which all Salesforce changes — regardless of origin — must pass before reaching production.

Getting Started: Practical First Steps

Organisations looking to close the admin-developer gap in their Salesforce DevOps practice should consider the following starting points:

1. Install the Salesforce CLI on admin workstations and walk admins through a basic retrieve-commit-deploy cycle in a sandbox environment.
2. Establish a shared Git repository for all Salesforce metadata — both code and configuration — with a branching strategy that all team members understand.
3. Set up a CI pipeline (using tools such as GitHub Actions, GitLab CI, or Jenkins) that runs automated Apex tests on every pull request.
4. Run collaborative training sessions where developers explain the pipeline to admins and admins explain their configuration workflows to developers. Mutual understanding reduces friction on both sides.
5. Treat automated test coverage as a shared metric. Make it visible to the whole team, and celebrate when admins contribute to improving it.

Conclusion

The separation between Salesforce admins and developers is a historical artefact of how the platform was originally positioned, not an inherent property of how it must be operated. As Salesforce environments grow in strategic importance, that separation becomes a liability. A unified DevOps practice — grounded in version control, automated testing, and a shared delivery pipeline — turns the admin-developer relationship from a potential conflict into a genuine force multiplier for the organisation.

The technical barriers are real but surmountable. The cultural and organisational investment required is modest compared to the risks of leaving them unaddressed.

Key Takeaways

The shift from project to product continues, with an eye towards long-term maintainability and adaptability.
The shift to small, semi-autonomous teams continues, as does the market for platforms that integrate and aggregate information from disparate tools.
The growing importance of data science and analytics on the development lifecycle.
Concepts such as Value Stream Mapping that look at the entire Dev lifecycle become increasingly important, and pre-built tools to support this become more common.
Increasingly specialized vendors and SI partner offerings arise to assist with DevOps needs.
The DevOps movement continues to grow in terms of its impact and influence in the IT ecosystem. There’s no question that the DevOps movement is maturing to the point that it has a power and a voice comparable to that of other movements such as cloud and agile. One interesting characteristic of the DevOps movement is that it arose from a wish within IT to optimize and harmonize the activities of different teams; but it’s matured to take into consideration the needs of end users and a holistic view of how organizations need to operate in order to be both flexible and efficient at scale.

Business leaders may complain that IT workers don’t appreciate the underlying business context in which they’re operating; and historically the IT department has been seen as a cost centre and not a value centre for organisations. The net result of that is that IT struggled to justify infrastructure and process improvements that would increase costs, unless they were specifically tied to a new business-facing initiative. But IT is becoming increasingly central to business operations. Done well, the flexibility of software allows organisations to adapt much more quickly than in a pre-digital infrastructure, thus making effective IT teams the biggest enablers of value.

Project to Product

Digital transformation has become an incredibly popular buzzword in recent decades, but it is essential for companies to recognize that digital transformation is not a one-time activity. The work done by Mik Kersten in his book ‘Project to Product’ highlights the fallacy of thinking of IT improvements as one-time projects. Digging into the history of technological revolutions and witnessing the most advanced manufacturing processes of today helped Kersten recognize that shifting to a product-focused mindset is a far more beneficial way for companies to frame IT.

What does it mean to approach internal IT initiatives as products? One aspect is recognising that IT systems entail long-term maintenance responsibilities, and are not something that should simply be created and turned over to an unrelated team. Another aspect is re-thinking the way we finance projects. The annual budgeting cycle is optimised for relatively stable systems, where change can be anticipated a year in advance. Teams vie for a share of the budget, and ensure that they never underspend their allocation, lest they receive less the following year. The requirement to get up-front funding tends to promote large, expensive projects, while budget for subsequent operation is often not factored in. The result of this are complex IT systems that are maintained by teams other than the ones who built them, that are excessively complex, and that often don’t meet the needs of end users. Agility needs to extend into every aspect of a business, from how teams are structured, to how work is financed, to how and when an organization decides to make improvements.

An example of a product-mindset is to have the team in charge of a CPQ (Configure – Price – Quote) system be responsible for both building and maintaining that system over time – the “you build it, you own it” mantra. The power of this mindset is in promoting stability and flexibility in what is built. When a team has long-term responsibility for building and maintaining the product, and for getting feedback on how it’s used in production, they greatly increase the chances of this product bringing lasting value to the organisation. Feedback on how systems are used in production is the essential input for ongoing improvement. It’s for this reason that in the DevOps community the assembly line motif of software development has given way to a circular or infinity loop motif for software development. The loop indicates that ongoing feedback from end-users is as important as the original specifications.

Product companies and product teams are by nature more stable than teams who just build and move on. Project teams work great in construction and civil engineering, where infrastructure rarely needs to change once built. But software allows for an utterly different level of agility. It can be replatformed or refactored without end users being aware; its design or functionality can be changed at the last minute before release to users; and different versions can be exposed to different users to solicit feedback. This agility is why the knowledge that went into creating a software product must be retained in the team if at all possible. There is a “cone of uncertainty” where at the outset of building something we know the minimum that we will ever know about it. As we proceed with development and a system comes under use, we necessarily learn things that could never have been predicted even by the most intelligent and thoughtful planning team.

Team Topologies

Related to this topic of “project to product” is the research that has been done around team cognitive load, and the importance of structuring teams in a way that optimises for communication and trust. There are both mathematical and empirical bases for saying that smaller teams that can act independently will perform more effectively than large teams or teams with many dependencies. Amazon’s two-pizza team size was not created to simplify their catering needs. It’s a wise structure that maximizes the power of teamwork while minimising the overhead required for constant coordination. Conway’s Law dictates that your architecture will come to reflect your team structure. Given that an optimal team size is constrained to five to ten people, modular architecture is essential if you want to be able to maintain your velocity over time. The idea is to give teams relative autonomy over particular systems. This feeds into topics like continuous integration which strives to reduce the impact of parallelisation. These kinds of DevOps best practices began with research and experimentation in the field, moved to being DevOps community lore, became substantiated through the research of teams like DevOps Research and Assessment, and will gradually become the basis for dedicated tools and entrenched ways of working.

Much of the tool growth that we are seeing today includes integration and Value Stream Management platforms such as Copado, Tasktop, Plutora, Xebialabs, GitLab and Cloudbees who are striving to bring information from many disparate systems together into one tool. I expect this integration and aggregation trend to continue as a practical way of dealing with the diverse reality of enterprise systems. In fact, teams benefit greatly from being able to choose their own tools. In the book ‘Team Topologies’ by Matthew Skelton and Manuel Pais, they refer to standardization as one type of monolith, “monolithic thinking”, that can interfere with maximum effectiveness. If you are striving to avoid monolithic thinking, but nevertheless need an integrated view of your systems and processes, data integrations are your only option.

Tool Evolution

LaunchDarkly showed that there is a market for products that facilitate particular DevOps practices, in this case separating deployments from releases. That practice is integral to activities like A/B testing and canary deployments, which have become recognised as powerful ways to reduce risk and enable experimentation. I would expect that more tools continue to appear to enable DevOps practices that otherwise would require custom coding.

Although this is not a new trend, definitely expect to see services like Amazon Web Services (AWS), Azure and Google Cloud Platform (GCP) continue to roll out new service offerings, compete with one another, and gain market share from legacy infrastructure. Azure and GCP are still playing catch-up in terms of the variety of products that they offer as well as the market share that they control, but expect them to be following close on the heels of AWS. Offerings such as kubernetes-as-a-service help to reduce the complexity of managing underlying infrastructure. Expect other kinds of complex systems to continue to be bundled into turnkey applications.

Data Science

The State of DevOps Reports from Puppet and Google have set the standard for using data science to evaluate the effectiveness of development practices. Expect to see more tools begin to integrate analytics and data science into their offerings. And expect to see more teams requesting and making use of such capabilities to facilitate experimentation and to validate the results of those experiments.

Business functions such as marketing have been using A/B testing and quantifying their effectiveness for many years. There are a huge number of marketing-oriented tools that have been highly tuned to give metrics on click through rate, adoption, time on site, return on investment, etc. The most long-standing of these is Google Analytics, but marketers have a vast range of tools to choose from. Ironically, IT teams are late to the party in terms of practices such as A/B testing and ensuring that applications are being adopted by users. It is often left to business teams to track adoption of an application created by an IT department. But internal IT departments are the ones who have the greatest opportunity to make improvements to meet the needs of users.

Expect to see tools for usage monitoring and even embedded feedback and user satisfaction surveys becoming more frequently used by internal development teams. These practices help close the gap between end users and development teams in the same way that marketing teams have been striving to close the gap between companies and their customers for many years. This kind of feedback is exceptionally powerful since there’s an inefficiency introduced by involving a business analyst every time you want to solicit user feedback. Selective feedback initiatives also suffer from sample bias.

Lean and Agile

DevOps is aimed at “actualizing agile” by ensuring that teams have the technical capabilities to be truly agile, beyond just shortening their planning and work cadence. Importantly, DevOps also has Lean as part of its pedigree. This means that there is a focus on end-to-end lifecycle, flow optimisation, and thinking of improvement in terms of removing waste as opposed to just adding capacity. There are a huge number of organisations and teams that are still just taking their first steps in this process. For them, although the terminology and concepts may seem overwhelming at first, they benefit from a wide range of well-developed options to suit their development lifecycle needs. I anticipate that many software tools will be optimizing for ease-of-use, and continuing to compete on usability and the appearance of the UI.

Whereas most early DevOps initiatives were strictly script and configuration file based, more recent offerings help to visualise processes and dependencies in a way that is easily digested by a broader segment of the organization. Especially as companies try to capture the attention and wallet share of CIOs, CTOs and other organizational decision-makers, reducing the difference between the actual UI of a tool and how it can be visualized and marketed becomes even more important. The most beautiful and graphical tools just sell themselves (to both business and technical users). Value Stream Mapping and delivery pipelines are particularly popular and effective ways to visualize the delivery process, while also providing day-to-day access to metrics and monitoring.

System Integrator

Finally, it’s clear that the market and demand for practices such as DevOps exceeds the availability of skilled people to implement it. Thus system integrators will continue to have a role in helping teams to ramp up on these technologies and processes, and in some cases to even help manage the development lifecycle for organizations. Research in the State of DevOps Reports indicates that functional outsourcing is predictive of low performance, so organizations should be very careful about delegating one particular activity such as testing or deployment to an outsourced contractor. Unless done carefully, functional outsourcing goes against the spirit of DevOps which focuses on bringing all of the relevant stakeholders (from Dev to Ops) together with aligned goals, shared visibility, and shared technologies.

Consulting partners are an extremely powerful way of getting help in the absence of in-house talent. But they necessarily introduce organisational boundaries unless consultants are deeply embedded in the organisation, working long-term alongside full-time employees. Rely on DevOps consultants as enablers to help you to adopt technologies, improve your processes, design your metrics and so forth. But be careful about outsourcing a particular part of your process (such as testing) that is on the critical path to production.

It may work better to give the entire development process for a particular application to a consulting company. But consider the long-term lifecycle of this and how you want that company to maintain the application over time. In the spirit of project to product, there is risk in having one team build an application and a separate team maintain it. Think of the knowledge that you are building within the team as a critical part of your architecture. Just as it’s foolhardy to rip out a substantial chunk of your architecture just after go-live, so too it’s unwise to rip out a substantial chunk of knowledge from your team just after go-live.

Summary

In summary, the DevOps movement continues to grow, flourish, and gain influence in the IT world and the business world at large. As our organisations become increasingly digital, the agility of our IT systems becomes critical to the life and health of our companies. DevOps as a movement blends together psychology, sociology, technical management, automation, security, and practices such as lean and agile to optimise an organisation’s ability to thrive in a digital world. The consulting, tooling, infrastructure, and training ecosystem to support this is still evolving. The market for DevOps is in fact the market for digital success, thus expect continued growth through 2020 and beyond.

What Is Cloud AI Platform Pipelines?

Google announced the beta of Cloud AI Platform Pipelines, a managed service designed to help data scientists and ML engineers deploy robust, repeatable machine learning pipelines on Google Cloud. The service adds monitoring, auditing, version tracking, and reproducibility to ML workflows that would otherwise be difficult to operationalize at scale.

The core motivation is familiar to anyone who has moved an ML project from experimentation to production. As Ramesh and Unruh wrote in the original blog post:

When you’re just prototyping a machine learning (ML) model in a notebook, it can seem fairly straightforward. But when you need to start paying attention to the other pieces required to make an ML workflow sustainable and scalable, things become more complex.

Cloud AI Platform Pipelines is Google’s answer to that complexity — and its entry point into the broader practice of MLOps.

MLOps: DevOps Principles Applied to Machine Learning

MLOps (Machine Learning Operations) applies DevOps principles — automation, monitoring, version control, and continuous delivery — to the ML lifecycle. A typical ML workflow subject to MLOps practices covers:

• Data preparation and validation
• Feature engineering and analysis
• Model training and evaluation
• Model deployment and serving
• Ongoing monitoring and retraining

Cloud AI Platform Pipelines is designed to be the orchestration layer that ties these stages together in a repeatable, auditable manner.

Architecture: How It Works

Cloud AI Platform Pipelines runs on a Google Kubernetes Engine (GKE) cluster and is accessible through the Cloud AI Platform dashboard in the Google Cloud Console. The service has two major parts:

• Infrastructure layer: Deploys and runs structured AI workflows integrated with GCP services including BigQuery, Dataflow, AI Platform Training and Serving, and Cloud Functions.
• Pipeline tooling layer: Tools for building, debugging, and sharing pipelines and individual pipeline components.

Workflow Orchestration with Argo

Under the hood, the platform uses Argo, an open-source, container-native workflow engine for orchestrating parallel jobs on Kubernetes. Argo executes each pipeline on individual isolated pods within the GKE cluster. This architecture allows pipeline components to leverage managed Google Cloud services such as Dataflow, AI Platform Training and Prediction, and BigQuery. Pipelines can also include steps that run large-scale GPU and TPU computation directly in the cluster, taking advantage of GKE features like autoscaling and node auto-provisioning.

Automatic Metadata Tracking with ML Metadata (MLMD)

Every pipeline run benefits from automatic metadata tracking via the ML Metadata (MLMD) library. MLMD logs:

• Artifacts consumed and produced at each pipeline step
• Pipeline parameters used during each run
• Lineage linking input and output artifacts across steps

MLMD is included as part of the TFX SDK but can also be used independently in other contexts.

Choosing an SDK: KFP vs. TFX

Users can define their pipelines using one of two SDKs:

Regardless of which SDK is used, the compilation and submission process is the same: the SDK compiles the pipeline definition and submits it to the Pipelines REST API. The REST API server then stores and schedules the pipeline for execution. Google noted in the original blog post that the two SDK experiences were planned to converge over time.

Key Features for Enterprise ML Workloads

According to the announcement, customers using Cloud AI Platform Pipelines receive:

• Push-button installation directly from the Google Cloud Console
• Pipeline versioning to track changes to pipeline definitions over time
• Automatic metadata tracking of artifacts and executions via MLMD
• Cloud Logging integration and built-in visualization tools
• Seamless integration with BigQuery, Dataflow, AI Platform Training and Serving, Cloud Functions, and other GCP managed services
• Prebuilt pipeline components for common ML workflow steps, plus support for custom components

Portability: The Kubeflow Advantage

Because Cloud AI Platform Pipelines is built on Kubeflow Pipelines — which is open-source and runs on standard Kubernetes — workloads are not strictly locked into Google Cloud. A commenter on a Hacker News thread discussing the announcement noted:

Cloud AI Platform Pipelines appear to use Kubeflow Pipelines on the backend, which is open-source and runs on Kubernetes. The Kubeflow team has invested a lot of time on making it simple to deploy across a variety of public clouds, such as AWS and Azure. If Google were to kill it, you could easily run it on any other hosted Kubernetes service.

This is a meaningful consideration for teams evaluating cloud vendor lock-in risk in their ML infrastructure decisions.

Planned Features at Beta Launch

At the time of the beta announcement, Google outlined the following features on its near-term roadmap:

• Easy cluster upgrades
• Additional templates for authoring ML workflows
• Simplified UI-based setup of off-cluster storage for backend data
• Workload Identity support for transparent, credential-free access to GCP services
• Multi-user isolation — allowing each user to control access to their own pipelines and resources within a shared cluster

Context: Google’s Broader MLaaS Strategy

Cloud AI Platform Pipelines is part of Google’s broader Machine Learning as a Service (MLaaS) portfolio, which includes Cloud AutoML, Kubeflow, and AI Platform Prediction. The service positions Google more directly against competing managed ML platforms such as Amazon SageMaker and Azure Machine Learning.

For DevOps and platform engineering teams tasked with supporting data science organizations, Cloud AI Platform Pipelines represents a significant step toward treating ML workflows with the same operational rigor as software delivery pipelines — with versioning, auditability, and reproducibility built in from the start.

Further Reading

For implementation details, refer to the official Google Cloud AI Platform Pipelines documentation and the Kubeflow Pipelines project on GitHub. The original announcement was covered in depth by InfoQ.

It’s taken just a couple of decades for humanity to develop a digital reflex. The consulting of connected devices has become second nature, and software applications are now the primary interface between a business and its customers. This means app performance has become business-critical. Customers demand speed, accuracy and accessibility. And apps are the primary source of the digital era’s most precious resource — data.

When apps don’t work, customers go elsewhere. Fast. And when customers move to a competitor’s app, that competitor gains their data value, as well as their business.

“We know, from our app retention index, that 50% of users are willing to pay more for a competitor’s product if it performs and gives them a better experience,” said Danny Winokur, general manager at AppDynamics, a Cisco company.

Even worse, 63% of users in AppDynamics’ app-retention index not only drop the app, but tell everyone else to drop it as well. And these days, it doesn’t take many influencers spreading bad reviews on social media to affect a company’s brand reputation.

Keep an eye on your apps

AppDynamics was founded in 2008 to provide digital companies with a way to monitor and optimize their real-time business performance. Its Application Intelligence Platform was unique, in that it used digital agents and bots to track the source of slow-downs across the entire application ecosystem.

The company received several successful rounds of filing, and by 2017 it had an established product portfolio underpinned by the enhanced Application Intelligence Platform. Its strategy was simple: “Applications are really becoming the foundation of the modern business, and understanding the performance of those applications, the users, the application, the business metrics themselves is critical, as this really signifies the brand for most companies,” said Thomas Wyatt, chief product officer at People.ai and previous chief strategy officer at AppDynamics.

Hopes for a rebirth in tech initial public offerings were kindled when AppDynamics filed for an IPO in 2017. Then, just one day before its market debut, Cisco Systems Inc. acquired the company at a massive $3.7 billion price tag. This was more than twice what it had been estimated to gain from its IPO. The surprise switch even had some wondering if AppDynamics had pulled a “classic M&A fake” to lure potential buyers.

It was a bold move. Cisco is known for its aggressive acquisitions policy, but at the time, the company was undergoing an internal restructuring as it prepared to take on the cloud market. Rowan Trollope, then senior vice president and general manager of internet of things and Applications at Cisco justified the strategy, saying, “We acquired AppDynamics because they are a market leader in a category that will be a cornerstone for how enterprises drive their business forward, Together, AppDynamics and Cisco will be the only company that can deliver complete visibility spanning from the infrastructure to application to end user.”

Application performance management is a growing market … and so is cloud computing
The prediction was sound. In the year after Cisco purchased AppDynamics, the Application Performance Management Software Market grew 13.4% to annual revenue of $4.3 billion. From $4.3 billion in 2018, the market grew to $5.8 billion in 2019. In addition, research firm Mordor Intelligence predicts the market will continue to grow at 12% CAGR from 2020 to 2026 to a value of $11.43 billion.

But more important than a financial investment, AppDynamics has proved to be central to Cisco’s play in the complex market of hybrid and multicloud.

The company seems to have an uncanny knack for making purchases that chart the course of enterprise computing. Competing with the cloud-native tech giants has caused many traditional tech companies to struggle, and Cisco is no different. As software increasingly replaces traditional hardware, Cisco has expanded its portfolio in a way that supports its position as a leader in infrastructure while moving toward software-centric solutions.

The AppDynamics purchase was a ground-floor investment that cemented the company’s commitment to cloud-native architecture. Since then, Cisco has purchased companies with a view to combining their cloud-native savvy with its existing expertise. Some examples are software-defined networking startup Viptela Inc. and hyperconverged infrastructure vendors Springpath Inc. and Skyport Systems Inc.

Cisco’s cloudification

With compute all but virtualized, cloud is moving to the next level in its quest to turn traditional hardware software-centric: Network virtualization.

“The public cloud itself now has become such a super-compute system that it’s like a massive computer. And, of course, a massive computer has massive networking,” said Saar Gillai, independent board director and chief executive officer adviser and former senior executive at Cisco Systems Inc. and Hewlett Packard Enterprise Co.

With edge computing, 5G, and WiFi 6 due to debut in 2020, next-generation networks hold the promise of fulfilling the potential of the internet of things.

“Traditional networking companies such as Cisco are hoping their strength in data centers and corporate networks along with newly developed and acquired 5G and other edge technologies can help them remain a mainstay in enterprises as they rethink their networks,” stated Wikibon’s Paul Gillin in a special report on NextGen Networks for SiliconANGLE.

Once again, AppDynamics is helping Cisco join the dots. Over the past three years, Cisco has been integrating AppDynamic’s technology throughout its portfolio.

“The whole point now is about multidomain and actually connecting the different layers of the technology stack to produce insights,” said Winokur. “The winners in an experience-driven world are those that can implement with velocity, not break things, and deliver well-designed, beautiful experiences.”

Cisco guides AppDynamics’s strategy from performance monitoring to customer journey analysis
AppDynamics has made several key changes in its offerings over the past three years. And all have the common focus of supporting Cisco as it reboots for a hybrid and multicloud world.

Rather than focusing on monitoring performance at the application level, the platform has adapted to the needs of the digital business by providing common tools across the technology stack. “As changes in the market push [teams] to work together, they need common tooling and common data sets,” Winokur said.

As part of this shift, AppDynamics updated its platform to provide Kubernetes monitoring. Integrating the machine-learning technology of Cisco acquisition Perspica Inc. led to the creation of the Cognition Engine. And Experience Journey Maps provides end-to-end visibility into the customer journey as they navigate through an app.

Strategic acquisitions are the not-so-secret game plan that have kept Cisco relevant as other tech pioneers have fallen by the wayside. And AppDynamics is a lynchpin, keeping the company in a strong position to remain top of the heap as network joins compute in the virtualized world of cloud.

“This is about Cisco setting the table,” said John Furrier, co-host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during his opening day analysis of the 2020 Cisco Live EU event. “To me, the top story so far is that Cisco is not going to yield to the big cloud guys. They are, brick by brick, rebooting their products to be cloud-enabled for hybrid and then ultimately multicloud.”

What Is Cloud AI Platform Pipelines?

Google Cloud Platform (GCP) announced the beta launch of Cloud AI Platform Pipelines, a managed product for automating and orchestrating end-to-end machine learning (ML) workflows. The product is built on two open-source technologies: TensorFlow Extended (TFX) and Kubeflow Pipelines (KFP), and runs on a Google Kubernetes Engine (GKE) cluster.

The core problem Cloud AI Platform Pipelines addresses is the complexity of managing the full ML lifecycle — from ingesting raw data, through model training and evaluation, to serving predictions in production — in a repeatable, auditable way.

Key Features

• Repeatable ML pipelines: Define pipeline steps once and re-execute them consistently across runs.
• Artifact and lineage tracking: Track the history, versions, and provenance of data artifacts produced at each pipeline stage using ML Metadata (MLMD).
• Monitoring and auditing: Built-in observability for pipeline runs and model versions.
• GCP service integration: Native connectors to BigQuery, Dataflow, and other GCP services.
• Managed infrastructure: Google manages the underlying GKE cluster, reducing operational overhead for teams.
• Dual SDK support: Teams can use the higher-level TFX SDK or the lower-level KFP SDK; Google has stated plans to merge both into a single TFX SDK.

Architecture Overview

Understanding how Cloud AI Platform Pipelines works requires understanding its two foundational layers:

TensorFlow Extended (TFX)

TFX is a high-level abstraction layer for defining ML pipelines. The central concept is a pipeline — an ordered series of data transformation steps called pipeline components. Data passed between components are called artifacts. TFX also defines and manages an ML Metadata (MLMD) store, enabling version tracking and lineage for both pipeline runs and the artifacts they produce.

TFX was first documented publicly in a paper Google presented at KDD 2017, which described the internal ML platform Google built to cover all phases of the ML process: data analysis, transformation, training, evaluation, and production inference. The original execution engine was Apache Beam — itself derived from Google’s internal Flume system and the technology underpinning Google Cloud Dataflow. TFX continues to use Beam for defining data-parallel operations, and now also supports Kubeflow Pipelines and Apache Airflow as orchestration engines.

Kubeflow Pipelines (KFP)

KFP acts as the orchestrator. It schedules and executes each TFX pipeline component as a pod on the GKE cluster, coordinating execution order and passing artifacts between steps. Google open-sourced Kubeflow in 2018 as an ML-specific platform for Kubernetes. Spotify later adopted Kubeflow as their standard ML platform and open-sourced their Terraform templates for cluster provisioning.

Relationship to Other GCP Orchestration Tools

Before Cloud AI Platform Pipelines, Google had recommended Apache Airflow (and its managed GCP counterpart, Cloud Composer) for orchestrating ML workflows. Airflow and Cloud Composer are general-purpose workflow tools, not ML-specific. Cloud AI Platform Pipelines narrows the focus: it is designed specifically for ML pipelines and abstracts away cluster management entirely, removing a significant operational burden from data engineering and MLOps teams.

The Broader MLOps Ecosystem Context

The launch touches on a broader competitive dynamic in the ML infrastructure space. As one commenter noted in a Hacker News discussion on the announcement:

The battle of ML pipeline ecosystems is the engine — not so much the API. It had been Beam vs [Apache] Spark. Now, Google are changing tack and saying it is TensorFlow on Kubernetes with distributed processing vs Spark-based ML pipelines.

This framing is useful for teams evaluating ML platforms. Cloud AI Platform Pipelines represents Google’s bet on a Kubernetes-native, TensorFlow-centric execution model, in contrast to Spark-based ML pipeline approaches favored in other ecosystems (such as those built on Databricks or Amazon EMR).

What This Means for MLOps Engineers

• Reduced infrastructure overhead: Google manages the GKE cluster, so teams can focus on pipeline logic rather than cluster operations.
• SDK choice matters short-term: The TFX SDK is higher-level and more opinionated; the KFP SDK offers more flexibility. Google’s plan to merge them means teams should monitor SDK deprecation announcements.
• Kubeflow familiarity is transferable: Teams already working with open-source Kubeflow will find the concepts familiar; the managed offering primarily removes cluster setup and maintenance steps.
• Lineage and reproducibility are first-class: The MLMD integration makes this a stronger choice than generic workflow tools for teams that need audit trails for model versions and training data.
• GCP lock-in considerations: Deep integration with BigQuery and Dataflow increases productivity on GCP but should be weighed against multi-cloud or portability requirements.

Summary

Cloud AI Platform Pipelines (announced in beta in early 2020) brought a fully managed, ML-specific pipeline platform to GCP, combining the abstraction of TFX with the Kubernetes-native orchestration of Kubeflow Pipelines. For teams already invested in the GCP and TensorFlow ecosystem, it offered a significant reduction in operational complexity while providing enterprise-grade lineage tracking, reproducibility, and monitoring out of the box.

What Are Containers and Why Does Kubernetes Matter?

Containers are self-contained software packages that bundle an application together with all of its dependencies — libraries, runtime, configuration — so that the application runs consistently regardless of the underlying computing environment. This makes containers ideal for moving workloads between a developer’s laptop, a test environment, an on-premises data centre, and public cloud infrastructure without environment-specific issues.

But containers introduce their own operational complexity. Running dozens or hundreds of containers in production requires automated tooling to handle deployment, health checks, scaling, networking, and rolling updates. That is the problem Kubernetes solves. Originally developed at Google and now maintained by the Cloud Native Computing Foundation (CNCF), Kubernetes is an open-source container orchestration system that automates these operational concerns at scale.

Together, containers and Kubernetes give engineering teams workload portability and the orchestration layer needed to operate that portability reliably in production. The following real-world case studies illustrate what that looks like in practice.

Expedia Group: Accelerating Travel Services with Containers and EKS

Expedia Group began adopting microservices architecture and AWS public cloud services in 2013. In 2015, the company started deploying containers via Amazon EC2 Container Service (ECS) to run microservices workloads within AWS. The primary drivers were application portability across multiple environments and faster time-to-market for new products and features.

“With containers spinning up in seconds, the amount of time that is needed to ship a new feature to our customers has significantly reduced, and we are able to accelerate our speed to market.”

— Kuldeep Chowhan, Distinguished Engineer, Vrbo (a unit of Expedia Group)

Today, thousands of applications run in production on ECS across multiple AWS regions, giving Expedia the high availability needed to serve a global customer base without disruption.

From Apache Mesos to Kubernetes

In 2015, the Vrbo brand adopted Apache Mesos to manage container workloads spanning both on-premises data centres and AWS. This hybrid approach allowed Vrbo developers to run identical workloads in both environments, accelerating the eventual migration from on-premises infrastructure to the cloud.

As Kubernetes grew in adoption across the industry, Expedia’s Hotels.com unit moved hundreds of its applications onto Kubernetes running in AWS. After Amazon launched its managed Elastic Kubernetes Service (EKS), multiple teams across Expedia Group — including data science, online experiences, and streaming — began adopting it as their standard container orchestration platform.

“Longer term, that is the direction Expedia Group is planning on heading for container runtime and orchestration needs.”

— Kuldeep Chowhan, Vrbo

Measurable Business Impact

The shift to microservices and containers produced measurable improvements in engineering velocity and infrastructure efficiency:

• Release velocity: The Vrbo brand reported a 12-times increase in release velocity over three years.
• Deployment frequency: New features that previously took months to ship can now be released multiple times per day.
• Targeted scaling: Instead of scaling an entire monolithic application to handle traffic spikes, teams can now scale only the specific microservices experiencing elevated demand, reducing infrastructure costs.
• High availability: Distributing containerised workloads across multiple AWS regions means customers worldwide can access Expedia’s travel services without service interruptions.

Operational Realities

Chowhan was candid that containers do not eliminate infrastructure management entirely. Container runtimes and orchestration platforms require their own updates, and those updates must be rolled out across the server fleet running the platform. Development tooling, practices, and engineer skill sets also had to evolve continuously — from source code toolchains to runtime operations — as the container ecosystem matured.

“We had to retool to be adapt in the new ecosystem across our travel platform, and also train our engineers with the evolving ecosystem across containers.”

— Kuldeep Chowhan, Vrbo

Primerica: Modernising Financial Services with a Hybrid Cloud Strategy

Primerica, a financial services and insurance firm, built a Kubernetes environment to scale applications on a private cloud built on IBM software running on Nutanix hardware. Docker containers provide application consistency between development, test, and production environments — a critical requirement when migrating from a traditional technology stack to a modern hybrid cloud architecture.

“This is a key strategic component in our migration from our traditional technology stack to our modern hybrid cloud.”

— Barry Pellas, CTO and Executive Vice President of Application Development and Delivery, Primerica

By standardising on containers, Primerica gains environment parity — code that behaves the same way in development behaves the same way in production. This consistency is particularly valuable in regulated industries such as financial services, where predictable, auditable application behaviour matters as much as delivery speed.

Key Takeaways for DevOps Teams

The experiences of Expedia Group and Primerica highlight several patterns that recur across successful container and Kubernetes adoptions:

• Start with portability as a goal. Containers deliver the most value when teams have a concrete multi-environment or multi-cloud problem to solve — not as a technology-first initiative.
• Plan for orchestration from the start. Running containers in development is straightforward; running them reliably at scale in production requires Kubernetes or a comparable orchestration layer from the outset.
• Invest in engineer training. Adopting containers and Kubernetes requires reskilling. The toolchain — CI/CD pipelines, observability, security scanning — changes significantly from a traditional VM or bare-metal workflow.
• Infrastructure management does not disappear. Managed services such as EKS reduce the operational burden of running Kubernetes, but platform updates, cluster upgrades, and node management remain ongoing responsibilities.
• Microservices and containers are complementary. The ability to scale, deploy, and update individual microservices independently is a primary driver of the engineering velocity gains these organisations reported.

As multi-cloud and hybrid cloud strategies become standard rather than aspirational, containers and Kubernetes are increasingly the common infrastructure layer that makes workload portability a practical reality rather than an architectural ideal.

Original source reporting: sg.channelasia.tech (2020)

Why AIOps Needs a Structured Deployment Plan

Applying artificial intelligence and machine learning to IT operations — commonly called AIOps — promises faster incident resolution, smarter alert management, and reduced operational noise. The category has produced a wave of specialist vendors and significant enterprise interest. Yet many IT leaders are rightly cautious: an AIOps tool that misfires early can erode both team confidence and executive sponsorship in ways that are hard to recover from.

The antidote is process. Before you license, configure, or run a single model, you need a clear workflow that takes the project from problem definition through to production and continuous refinement. The following seven steps provide that structure.

Step 1: Plan

Resist the temptation to boil the ocean on day one. Choose one or two specific use cases that have measurable pain points and a team that is genuinely open to change. Common starting points include alert noise reduction, automated ticket routing, and anomaly detection on a bounded service or application tier.

During planning, address the following areas:

• Skills inventory: Identify where you have gaps in data science, automation engineering, DevOps, and continuous integration. IT Ops staff need enough working knowledge of machine learning to audit model behaviour once automated control is active — they do not need to become data scientists, but blind trust in a model is risky.
• Workflow impact: Map the operational changes required for your chosen use case. For example, if the AI system is correlating alerts across an e-commerce platform, define what happens when correlated alerts span multiple teams and who owns the escalation path.
• Data readiness: Understand what data the tool requires and whether that data is available natively or needs to be sourced from a CMDB or another system of record. Missing context at ingestion time will degrade model accuracy.
• Goal setting: Define measurable targets — for example, a percentage reduction in alert volume, a reduction in mean time to resolve (MTTR), or a decline in ticket count. Establish how results will be measured and communicated to stakeholders.
• Model training expectations: Document what trainers and operators should expect during the early weeks, when the ML model is still immature and generating lower-confidence outputs. Setting realistic expectations early prevents early abandonment.

Step 2: Socialize

Technical readiness means little if the people affected by the system are not brought along. AIOps triggers legitimate concerns about job displacement, changed responsibilities, and loss of control over operational decisions. Address these concerns directly and early.

Explain how the system works at a conceptual level, what problems it is solving, and how it will change day-to-day workflows. Identify internal evangelists — operators or engineers who understand the technology and are willing to advocate for it — and empower them to answer peer questions and support informal training. Broad organizational buy-in reduces resistance when the system enters production and makes feedback loops faster and more honest.

Step 3: Understand the System’s Capabilities

Before configuration begins, invest time in learning how the chosen AIOps platform actually works, not just what the vendor claims it does. Focus on:

• Data dependencies: Some use cases require additional data to function correctly. Alert correlation, for instance, may need topology or dependency mapping data to validate that a relationship between alerts is genuine rather than coincidental.
• Supported use cases: Confirm which problems the platform addresses out of the box. Common capabilities include anomaly detection, event correlation, alert suppression, and automated ticket routing. Prioritize the use cases most likely to produce quick, visible wins with the lowest operational risk.
• Model transparency: Understand how the platform surfaces its reasoning. Operators who cannot see why a conclusion was reached will struggle to audit or trust the system.

Step 4: Set Up and Observe

Configure the system for your selected use cases and put it into observe-only mode — the system watches live data, applies its algorithms, and generates suggestions, but takes no autonomous action yet. This phase serves two purposes: it validates that the configuration is correct, and it builds operator familiarity with how the system reasons.

A practical signal of solution quality emerges here: if the platform requires weeks of complex configuration before the ML layer can even begin producing outputs, that is worth flagging as a risk. Modern AIOps tooling should reach an observable state relatively quickly. Operators should be able to inspect how algorithms interact with incoming data and trace how specific conclusions were reached — transparency is not a nice-to-have, it is a prerequisite for trustworthy automation.

Step 5: Recommend — Controlled Automation in a Safe Environment

AIOps delivers compounding value when it handles routine, predictable events autonomously. In this step, IT Ops defines a category of well-understood, low-risk events — such as VM utilization threshold breaches or known patch update sequences — and configures the system to apply a standard remediation automatically.

Run this automated handling in a sandbox or against a non-critical workload first. Monitor results closely. The goals at this stage are to verify that automated responses are accurate, that standard procedures are being followed consistently, and that automated handling is not inadvertently masking conditions that would otherwise escalate. Success here is the foundation for expanding automation to higher-stakes workloads.

Step 6: Deploy to Production

Once pilot results are acceptable and stakeholders are comfortable with the recommendations the system is producing, move to a production deployment. Do not treat this as a hard cutover. Run the system in a shadow or test mode alongside production operations for at least two weeks, confirming that outputs remain accurate under real load and that operators find the guidance actionable.

Collect structured feedback from users during this period. Issues that did not surface in the controlled pilot environment will appear under the variety of real-world conditions, and catching them before full autonomy is granted limits operational impact.

Step 7: Review and Refine

After a few weeks of production operation, measure results against the goals established in Step 1. If the target was alert noise reduction, quantify the change. If it was MTTR improvement, compare pre- and post-deployment baselines. Share results transparently with stakeholders.

Supplement quantitative metrics with qualitative feedback. Survey operators about their experience: what is working, what is creating friction, and what they wish the system did differently. Use this input to retrain the model, adjust configurations, or — once the initial use cases are performing well — expand to additional use cases.

AIOps is not a deploy-and-forget technology. The operational environment changes, new alert patterns emerge, and model drift is a real phenomenon. Building review cycles into your operating rhythm ensures the system continues to deliver value rather than quietly degrading.

Summary: The 7-Step AIOps Deployment Framework

AIOps implementations do not need to be high-risk undertakings. Breaking the project into discrete, verifiable stages — each with clear criteria for moving forward — keeps the team in control, builds trust incrementally, and produces a system that operators genuinely rely on rather than work around.

What Are Google Cloud Machine Images?

Google Cloud introduced a feature called Machine Images designed to make it significantly easier for system administrators and data scientists to set up, replicate, and maintain specialized virtual machine (VM) environments in the cloud.

At its core, a machine image stores all the information required to fully restore a Compute Engine VM instance. This goes beyond what Google Cloud’s older custom images feature offered. While custom images are limited to a single boot disk, machine images capture:

• All attached disks (boot and data disks)
• Instance configuration properties
• Instance metadata
• Associated IAM permissions

This comprehensive snapshot means a machine image represents a complete, reproducible environment — not just an OS image.

Why This Matters for Data Science and DevOps Teams

Data science workloads typically require carefully configured environments: specific library versions, GPU drivers, mounted datasets, and fine-tuned system settings. Manually reproducing these environments across multiple cloud instances is time-consuming and error-prone.

Machine images eliminate this friction. Once a VM has been configured to the desired state, teams can capture it as a golden machine image and use it as the authoritative baseline for spinning up additional instances — all configured identically to the source.

According to Ari Liberman, a product manager for Google Compute Engine, machine images are designed to sit at the heart of scalability, backup, and disaster recovery strategies. They use the same differential disk backup technology that powers incremental snapshots, making instance backups both reliable and cost-effective.

The “Golden Image” Workflow

Liberman illustrated the intended workflow with a practical example:

“Imagine that you’ve created and configured an instance as part of your web application, and you want to use it as the basis of other instances. With machine images, you can capture your web application exactly as you want it and save it as a golden machine image. You can then use that machine image to launch as many instances as you need, configured in the exact same way as your source instance. You can also share the machine image with other projects.”

Ari Liberman, Product Manager, Google Compute Engine

While the example references a web application, the same pattern applies directly to data science and ML engineering contexts — for example, capturing a configured Jupyter environment with CUDA drivers and Python dependencies installed, then sharing it across a team or replicating it for parallel experiment runs.

Override Functionality: Flexibility Without Sacrifice

Machine images also support override functionality, allowing individual properties of new VM instances to be selectively modified at launch time. This means teams can use a shared golden image as a base while still customizing things like machine type, network tags, or startup scripts on a per-instance basis — without altering the source image itself.

Key Use Cases

• Scalability: Rapidly launch multiple identical VM instances from a single source image to handle increased workloads.
• Backup and recovery: Capture point-in-time snapshots of full VM environments, including all attached disks, for reliable disaster recovery.
• Environment standardization: Share a validated, reproducible VM configuration across teams and projects.
• Onboarding: Provide new team members with a pre-configured environment instantly, reducing setup time.

Machine Images vs. Custom Images vs. Snapshots

Note: This comparison reflects the state of these features as of the March 2020 announcement. Google Cloud feature capabilities may have evolved since then.

How to Access Machine Images

At the time of announcement, Machine Images were available in beta and could be accessed through:

• The Google Cloud Console
• The gcloud CLI
• The Compute Engine REST API

For the current status of this feature and up-to-date documentation, refer to the official Google Cloud Machine Images documentation.

Why MLOps Exists

Machine learning models learn patterns from training data and then apply those patterns to new, unseen inputs. Managing that data safely—ensuring it is clean, unbiased, private-compliant, and representative of real-world conditions—is genuinely hard. It is widely cited (including by Gartner) that the majority of data science projects never reach production. The operational complexity of managing data, model versions, and live predictions is a significant contributing factor.

Real-world data changes continuously. A model trained on historical house prices in one economic climate may perform poorly after a market shift. Inputs and predictions must therefore be monitored over time for distribution drift—a challenge that simply does not exist in the same form for a traditional web service.

How MLOps Differs from DevOps

Conventional DevOps is built around the build-and-release cycle. Source code is compiled into executable artifacts, tested, and deployed. The non-code assets involved—configuration files, environment variables—are relatively small and static. The system behaves deterministically: given the same input, it produces the same output.

Machine learning inverts this model. Instead of encoding rules explicitly, a model infers weighted patterns from large datasets. A classic example is predicting house prices: the model is trained on known sale prices alongside features such as location, square footage, and number of bedrooms, and then applied to new properties. The “build” in this context is a multi-step pipeline that:

• Ingests and preprocesses potentially large volumes of training data
• Runs one or more training jobs, often with many hyperparameter combinations
• Produces a model artifact (e.g., a serialised set of learned weights)
• Evaluates the model against held-out test data
• Packages and deploys the artifact to a serving environment

This workflow is experimental by nature. Data scientists iterate on feature engineering, model architecture, and training parameters in ways that do not map neatly onto a conventional CI/CD pipeline.

Versioning and Reproducibility

DevOps has well-established practices for versioning code and rolling back releases. MLOps does not yet have equivalent standardisation for versioning the data used to train a model. If a deployed model behaves unexpectedly, being able to reproduce the exact training run—same data snapshot, same parameters, same environment—is essential for diagnosis. Without it, debugging a production model is extremely difficult.

Monitoring in Production

Standard application monitoring tracks error rates, latency, and resource utilisation. These metrics apply to ML services too, but they do not tell you whether the model’s predictions are good. Evaluating prediction quality in production is harder because:

• Ground truth (the correct answer) may only become available long after the prediction is made, if at all.
• Proxy signals—conversion rates, user feedback, downstream outcomes—must often be used instead.
• Data drift can degrade model quality gradually rather than causing a sudden, obvious failure.
• Training data may represent the general population well but miss important edge cases that appear in production.

Mitigating these risks requires careful monitoring design and a cautious, staged rollout strategy for new model versions—analogous to canary deployments in conventional DevOps, but with model-specific quality metrics rather than error codes.

The MLOps Tooling Landscape

A growing ecosystem of tools addresses different parts of the MLOps problem. Organisations broadly face two choices: adopt a managed platform or assemble an in-house stack from open-source components.

Managed Platforms

Cloud providers offer integrated ML platforms that handle infrastructure, experiment tracking, model registry, and deployment. Examples include AWS SageMaker and Azure Machine Learning. These reduce operational overhead but tie an organisation to a specific cloud provider. Vendor-neutral alternatives such as Databricks MLflow offer a managed or self-hosted option that is not bound to a single cloud.

Self-Assembled Open-Source Stacks

Organisations with niche requirements—specific data residency rules, integration with bespoke internal systems, or a need to avoid cloud vendor lock-in—may prefer to build their own platform from open-source components. The landscape is broad and can be difficult to navigate. The Linux Foundation’s LF AI & Data project maintains a landscape diagram and categorised project lists that can help map available tools to specific needs.

For teams already running Kubernetes, the Kubeflow project is worth evaluating. It curates a set of open-source ML tools—covering pipelines, experiment tracking, hyperparameter tuning, and model serving—and makes them work together on Kubernetes. The project is led by Google and has broad industry participation.

Governance, Explainability, and Regulatory Compliance

Reproducibility and monitoring are not just engineering concerns—they are governance concerns. A production ML system may need to satisfy auditors, regulators, or end users who ask why a particular prediction was made. In the European Union, the General Data Protection Regulation (GDPR) gives individuals a right to “meaningful information about the logic involved” in automated decisions that affect them. Similar requirements are emerging in other jurisdictions and sectors.

This creates a direct engineering requirement: the system must be able to explain its predictions. Modelling approaches are often categorised as:

• White-box models (e.g., linear regression, decision trees): the relationship between inputs and outputs is inherently inspectable and human-readable.
• Black-box models (e.g., deep neural networks): the internal mechanics are opaque, and additional interpretation techniques must be layered on top.

In highly regulated domains—medical diagnosis, credit scoring, criminal justice—the inability to explain a prediction may be a hard blocker. A medical diagnosis model, for example, may need to surface the specific features that drove a conclusion so that a clinician can evaluate it critically. This can constrain model choice entirely, ruling out approaches that would otherwise offer better predictive performance.

Tooling for post-hoc explainability of black-box models (approaches that explain individual predictions after the fact rather than by design) is an active research and engineering area. Capabilities in this space are expanding, but they should be evaluated carefully for the specific regulatory context rather than assumed to satisfy compliance requirements automatically.

Where MLOps Is Headed

MLOps is still maturing. Standardisation of practices—especially around data versioning, model registries, and production monitoring—lags behind what DevOps achieved for conventional software delivery. Different organisations have different ML use cases, and a single prescriptive workflow is unlikely to fit all of them.

What is clear is that machine learning systems cannot be treated as black boxes dropped into production and forgotten. They require the same operational discipline as any other production system—and additional discipline specific to the data and model lifecycle. As adoption grows and best practices solidify, expect to see the tooling converge and the gap between a data science experiment and a production-grade system narrow significantly.

This article is based on original reporting from The Register (theregister.co.uk, March 2020).