Today, IT and businesses often find themselves in silos working separately without a shared vision of the customer experience and all the services supporting it. IT must move beyond maintaining up a time to deliver the agility and competitive edge the business needs this means better cohesion between IT and business using predictive tools to prevent service failure meet Splunk IT service intelligence.

 IT service intelligence teams can no longer afford to be reactive especially when it comes to performance and availability. A protective IT strategy is necessary to get ahead of a problem and to protect revenue and your customer experience. Splunk IT service intelligence delivers a predictive IT, running an AIs we have AI scalable data platform that can ingest, search, analyze, and all types of data. ITSI allows you to go from reactive to predictive IT while gaining insights across both IT and business services. Splunk ITSI allows customers to predict service degradation 30 to 40 minutes in advance with the proactive alert for the KPIs most at risk so you can resolve incidents before they start gaining an end-to-end view of both IT and business KPIs on a single screen so you can work on what matters most when it comes to AI ops strategy. ITSI helps you become more proactive and even predictive reduce your incident investigation time business impact outages and event noise ensure your best customer experiences yes and keep the business and IT working better together with Splunk IT service intelligence. Put AI at the heart of your operations and try the free ITSI sandbox from today.

What are the benefits of Splunk IT Service Intelligence?

From the start as we are learning the series of Splunk software tools. In this stack, we discuss that every software has its own performances, capabilities, and security management but how the software works predictive and analytic. This is the reason behind endorsing the system software like how fast service is happening, and how the software is managing by the ITs team. With this effect, there are certain benefits related to Splunk ITSI admin as:-

• Intelligent incident management: Accelerate MTTR using real-time event correlation, automated incident prioritization and integrations with IT service management (ITSM) tools.
• Advance analytics & alerting: It manages a certain function as anomaly detection, adaptive thresholding, and predictive alerts & dashboards.
• Service analyzer: Service monitoring dashboards that display dependent systems and apps. Drill- down into a service’s underlying components to determine what’s impacting its health score.
• End to End visibility: protect business performance by monitoring service health, drill down into underlying infrastructure, all from one dashboard. 

Why we should learn Splunk IT Service Intelligence?

Nowadays, in this trending world. You have met with so many tools in the IT software services. Whenever we talk about Splunk’s advanced services then intelligence plays a prominent role in the IT entities. Now, we are talking about the learning conditions for IT service intelligence. So, what are the reasons to implement a Splunk ITSI? Before we had traditional solutions and those solutions were created as operational challenges like lack of visibility, complex environment, & slow response times. The most important talk is that lack of intelligence service created a significant impact on a business like lost revenue, inefficient productivity, poor customer experiences, & struggle to innovate then it creates technical advance in the field of IT sectors on the basis of Business and IT Service Monitoring, Intelligent Incident Management, Machine Learning, and Predictive Analytics. I wish that these awareness scenarios will give you brain mapping concepts for the Splunk intelligence certified admin.

How our Splunk IT Service Intelligence course/training would help?

This tutorial will help you about understanding the core features of Splunk ITSI like IT event management, service monitoring, and advanced analytics & alerting. It will give you a balancing ideology in the field of KPIs, Data sources, entities, services, incident response, automated remediation, predictive analytics, and event analytics. Through these interactive lectures and hands-on challenges, you will be able to learn to use IT service intelligence, services & glass tables, Notable event & event aggregation policies, and deep dives-investigation with swim lane views. After this, it gives you certification and that makes you qualified for the job opportunities along with the expected high-paid salary and makes your life efficient with future scope.

Related video:  

Dell Technologies today announced the general availability of Omnia, its open source platform for managing high-performance computing (HPC) and artificial intelligence (AI) workloads.

Omnia is an open source set of Ansible Playbooks developed by Dell’s HPC and AI innovation team in partnership with Intel and the broader HPC community. If this sounds familiar, Dell quietly released Omnia on Github earlier this year.

The platform automates the process of downloading and installing open source software for things like cluster management, application execution, accelerator drivers, frameworks, applications and platforms, and it manages systems running Slurm or Kubernetes, explained Caitlin Gordon, VP of product management at Dell Technologies.

“It’s all about simplifying and easing the development and the management of high-performance clusters for not just HPC but AI,” she said.

And because these automations are built on Ansible Playbooks, “you can compose and recompose the stack, based on what you need,” Gordon said. “We’re really taking an infrastructure as code approach here.”

This, she added, allows users to compose HPC deployments around a wide variety of workloads and hardware from multiple vendors. “Of course, because its open source, it can be shaped to whatever your needs are,” Gordon said. “We’ll continue to integrate the latest open source tools, but we also, even more importantly, have the collective power of the HPC community already working behind this.”

Arizona State University, one of Dell Technology’s HPC and AI centers for excellence, is among the first to deploy Omnia and begin contributing to its development, according to Gordon.

Dell HPC Nabs VMware Support, PowerEdge Gets Nvidia Upgrade
Dell Technologies also extended support for VMware to its HPC on-Demand program, which launched last year.

“Being able to consume things as a service be able to access resources as you need them, be able to move faster and simply pay for what you’re using. That’s really what HPC on demand is all about,” Gordon said. “It’s really about being able to support our customers who may not have the resources, or may not want to invest in their own infrastructure and people, because their HPC workloads maybe are inconsistent, and/or maybe they need to complement what they already have in their environment with a secondary environment to burst to.”

With support for VMware Cloud Foundation, VMware Cloud Director, and VMware vRealize Operations, customers can now extend these capabilities beyond single-tenant environments to include multi-tenant use cases.

Meanwhile, Dell Tech is adding Nvidia A10 and A30 GPUs to its PowerEdge server portfolio. “These are really ideal for things like VDI inferencing and interactive rendering,” Gordon said.

Amazon Web Services (AWS), an Amazon.com Inc. company, announced that Edelweiss has selected AWS as its preferred cloud provider, running the vast majority of its workloads on the world’s leading cloud to further drive its digital transformation efforts. To date, Edelweiss has migrated over 210 mission-critical applications from multiple business lines to AWS, including stock recommendations, public websites, and data models, to reduce cost, improve agility, and create innovative new services. Using the breadth and depth of AWS’s portfolio of cloud capabilities, including compute, machine learning, management and governance, serverless, and database, Edelweiss is building a digital ecosystem of applications and systems using application programming interfaces (APIs), which enable Edelweiss’s wealth, asset, credit, and insurance entities to securely exchange information and launch joint customer services. The digital ecosystem’s secure information exchange enables Edelweiss to gain greater customer insights, improve speed to market of new customer services, and help customers make better investment and financial decisions.

Each of Edelweiss’s multiple business groups previously ran their on-premises infrastructure in silos, which meant upgrading infrastructure and building new customer services was challenging and took engineers away from creating innovative new services for the company’s 1.45 million customers located across more than 100 cities in India and internationally. Running the on-premises infrastructure also meant high costs, consuming 80% of Edelweiss’s IT budget. As the company started transforming into an agile, data-driven enterprise by moving to AWS, it focused on dramatically reducing the costs associated with the undifferentiated heavy lifting of managing infrastructure. Edelweiss now spends 50% of its IT budget on driving digital innovation initiatives aimed at providing richer customer experiences at much lower costs, including a data mart which creates a unified view of the customer across Edelweiss’s business groups and an artificial intelligence (AI) stock simulation which provides predictions on stock performance to traders in real time.

With AWS, Edelweiss seamlessly scaled up its infrastructure for five days to process investments of more than US $2B during the launch of the Bharat Bond exchange Traded fund (ETF)—at 10% of the cost of a comparable on-premises system. By leveraging Amazon Elastic Compute Cloud (Amazon EC2), which provides secure, resizable compute capacity in the cloud, Edelweiss eliminated service downtime and minimized response times when customers invested into the ETF, making the Bharat Bond ETF India’s largest first-time bond ETF subscription offer launched by an asset management company. By centralizing and analyzing previously siloed customer data using AWS, Edelweiss can gain new customer insights and enable a more personalized customer experience. Using Amazon SageMaker, AWS’s service that helps data scientists and developers to build, train, and deploy machine learning models quickly, Edelweiss’s life insurance group has trained machine learning models to deliver personalized service and support to over 370,000 customers. For example, the models provide Edelweiss customer service agents with personalized insurance offerings which they can offer when engaging customers by phone or chat. Since adopting Amazon SageMaker, Edelweiss has reduced the time to launch new machine learning models from six months to only three weeks, and increased cross-sell rates by 300% and fraud detection rates by 35%.

“AWS is integral to Edelweiss’s strategy as we digitally transform from a traditional financial services organization to an agile, data driven enterprise,” said Nitin Agarwal, President and Group CIO, CTO, and Chief Digital Officer, Edelweiss. “We benefit from AWS technologies such as machine learning to create a digital ecosystem that connects our entities and enables them to co-deliver customer personalization at scale, all while maintaining the highest levels of regulatory compliance.”

“Edelweiss is using AWS to digitally connect its entities and radically change how they develop innovative services that help customers make better investment and financial decisions,” said Puneet Chandok, President, Commercial Business, India and South Asia, Amazon Internet Services Private Limited. “By selecting AWS as its preferred cloud provider, Edelweiss gains access to the broadest and deepest portfolio of cloud services to deliver new financial services faster and interact with customers and partners in a more seamless and secure way.”

Stevanato Group, a global provider of integrated containment and delivery systems to the biopharmaceutical and life sciences industries, has launched an Artificial Intelligence platform, based on Deep Learning (DL) models, designed to leverage the benefits of human-like decision-making in automatic visual inspection equipment. The platform is made to allow pharma companies to overcome the traditional trade-off between detection rate and false rejection rate, ensuring robust and replicable results. False rejects can be significantly reduced and the detection rate improved, yielding up to 99.9% accuracy, both for particle inspection and cosmetic defects detection.

DL is particularly beneficial when applied to difficult-to-inspect and high-value biotech drugs, as it allows pharma companies to achieve higher productivity while preserving drug integrity. Drugs in the form of suspensions or lyophilized cakes frequently challenge available vision tools, causing misinterpretations of supposed defects. Artificial Intelligence mitigates misclassification and reduces costly re-inspection, says the company.

Through its partnership with Microsoft and the adoption of Microsoft Azure platform, Machine Learning, and AI features, Stevanato Group intends to deliver “smart” equipment compliant with strict pharmaceutical data management and security requirements, while improving inspection performance and reducing costs related to production reparametrization. The company reports the certified cloud-based platform is compliant with U.S. CFR 21 Part 11 and EU GMP Annex 11, meets data integrity needs, and offers advanced monitoring tools such as heat maps and confusion matrix for model performance evaluation.

Incorporating new technologies can be challenging for pharma companies, as it usually requires them to adapt internal processes and invest time and resources. The company provides a team of vision and AI engineers to bring an accurate data analysis service with continuous support for tasks from image collection to model validation.

Key features of the platform include:

* Cloud-based: Data remains online and therefore perpetually available. The certified cloud-based platform that stores images and data can work with any cloud-based system and allows operators to manage images even if they are stored in the server.
* Security: Compliant with U.S. CFR 21 Part 11 and EU GMP Annex 11, the platform enables data sharing in a completely safe environment. Further, multifactor authentication and encrypted communication should ensure complete access control and data security.
* Assistance: Continuous support is available for all platform capabilities throughout the process, assisting pharma companies with a variety of tasks including a labeling assistant tool to optimize timing for classification and new recipe development.
* Monitoring: The platform features a wide range of statistics and visualizations (heat maps, confusion matrix, etc.) for model performance evaluation. Pharma companies should be able to track and monitor all processes through real-time reports.

As the global telecoms industry begins to deploy 5G networks, technologies such as cloud-native RAN and automation platforms will play major roles in their evolution. In this blog post, we’ll cover impact on management and orchestration, the challenges that emerge, our view on the future of management and orchestration and what Ericsson brings to the table.

5G evolution realizing new values
5G is more than radio and core. It encompasses virtualization, management and orchestration, transport, software-defined networking (SDN), and adaptive policies powered by artificial intelligence (AI) and machine learning (ML). As more and more technological innovations enter the market, 5G will continue to leverage the capabilities of these technologies to evolve further.

The 5G networks are designed to serve consumers and enterprises alike and as we add capabilities like network slicing, edge computing, service exposure and new RAN applications, we are in effect creating a new innovation platform. An innovation platform that will enable exciting new business models and use cases that will deliver innovative services to countless industry verticals.

The impact on orchestration
The architecture of the 5G network is substantially different from previous network generations. One key change is the introduction of micro services-based architecture and network functions distributed over different cloud infrastructures. In addition, many services will go across different network functions and domains in the network and they need to work in unison to ensure end-user experiences. This impacts the way we manage and orchestrate the networks.

The openness and disaggregation started in transport and core with the introduction of Software Defined Networking (programmable networks) and virtualization. A similar shift is expected to happen in the access domain and certain aspects need to be considered in the orchestration systems as well.

Open source and standards become even more important than previously in this context. Ericsson is a leading supporter of several open-source initiatives. These include the Open Network Automation Platform (ONAP) and Open Radio Access Network (O-RAN). We also work hard to encourage industry alignment to ensure speed of adaptation and realization of values from the 5G platform.

The challenges from an orchestration perspective
As service providers’ networks evolve, and new technologies are added, the network is becoming ever more sophisticated and complex.

As network functions are virtualized, networks will end up consisting of Physical Network Functions (PNF), Virtual Network Functions (VNF) and Cloud native Network Functions (CNF).
Network-related services and end-user services will flow across different network functions and multiple domains.
Services will become more advanced with the introduction of network slicing, exposure and RAN applications.
Multiple domains (radio, transport, core and cloud infrastructure) end up having their own OSS systems for different reasons.
The challenge that arises is that service providers need to deliver on stringent -service level agreements towards enterprises, with many different business models and multiple partners. Orchestration and management need to deliver on this challenging task.

What’s needed is an intent-based AI/ML-enabled automation platform that is open and highly flexible to scale. This Automation platform is key to manage these complex networks and services and to achieve the necessary network and service flexibility and speed up time to market.

As cloud-native RAN is introduced in the network, Ericsson brings the knowledge and capability from our experience of managing and orchestrating end-to-end 5G networks that includes cloud-native 5G core.

Key considerations for services providers
As Cloud RAN is introduced, certain aspects need to be considered in the management and orchestration systems.

Artificial intelligence and machine learning will play important roles in traffic steering and optimization decisions that will improve network efficiency and customer experiences and is an absolute prerequisite in next generation management and orchestration.

Since 5G will provide services to many vertical industries, the use cases demand Quality of Experience (QoE) such as high bandwidth, low latency, high security and ultra-reliability. Managing QoE proactively will be central to maintaining SLAs and continuous optimization is needed. The introduction of cloud-native RAN and Service Management and Orchestration will add new capabilities to assure best possible Quality of Experience.

The virtualized radio and core will be deployed on many different cloud infrastructures and distributed multiple data centers and far edges of the networks very close to where the services are being consumed. The orchestration system will need to work across all these different cloud environments and manage the end-to-end lifecycle management of network functions and services.

These are some key considerations that service providers need to think of as they evolve their management and orchestration. In the next section we will explore our view on how to realize this and the values we bring.

Ericsson’s view on next-generation management and orchestration
The next-generation management and orchestration (as defined by O-RAN Alliance) consists of a number of interworking components – the Service Management and Orchestration (SMO), Non-Real Time Radio Intelligent Controller, rAPPs and the interfaces O1, O2 and A1 allowing for communication between these components.

Service Management Orchestration
Ericsson brings their unique experience in building, deploying and managing cloud native networks and orchestration systems to enable seamless and modular introduction of Service Management Orchestration (SMO) for both greenfield and brownfield networks. We work closely with our customers to create unique use cases, while at the same time driving rapid innovation by enabling an open app ecosystem for both customers and partners. SMO the management and orchestration for the future and have a few very important characteristics:

AI/ML capabilities will be crucial in SMO for innovation, automation and operational efficiency. A key capability to achieve a high degree of automation is to use artificial intelligence and machine learning together with advanced policy management and we call that intent- based AI/ML.
To achieve a high degree of automation, Ericsson brings unique know-how to package AI/ML powered use cases into hardened and scalable solutions using our telco expertise.
SMO should have infrastructure visibility and be able to scale compute resources up and down. This also helps optimize or reduce the overall capacity costs.
SMO can handle end-to-end network slicing where RAN slicing is an important component. For example, a SMO can continuously monitor and ensure that a specific enterprise SLA is maintained in an end-to-end network slice that is providing a mission critical, low latency, ultra-reliable service.
SMO needs to be aware of applications and their performance, network behavior, traffic and multiple domain information, and have the ability to dynamically place application workloads across multiple cloud infrastructures.
The SMO need to simultaneously handle life cycle management of PNFs, VNFs and CNFs. In the cloud native world software management changes drastically with the introduction of high degree of automation, continuous integration/continuous development of micro-services. To support very fast introduction of new SW and features into the network, canary deployment and A/B testing will grow in relevance.
Non–real time RAN Intelligent Controller
The non-Real time RAN Intelligent Controller is key component of the future management and orchestration as defined by O-RAN. Placed inside the SMO layer it can access external information to dynamically use policies to optimize the RAN powered by AI and ML.

The non-Real time RIC plays a central role in SMO hosting the ecosystem of applications acting across RAN, Core and Transport, to generate and apply new policies and network optimizations. Additionally, from a pure innovation platform point of view, this component will expose the SMO capabilities and the network data, to a vast variety of applications, that will coexist and collaborate, to achieve new business outcomes.

Finally, it needs to be flexible, aware and secure. It needs to be flexible enough to foster innovation, aware enough to understand the network behavior, and secure enough, since it provides access to operators’ networks and data pools.

The power of RAN applications (rAPPs)
As seen above, rAPPs are powerful and important tools in the future management and orchestration. rAPPs can be written for a various management tasks such as assurance, optimization, automation, fault management, performance management, configuration management, software management, domain-specific applications.

The introduction of rApps, made possible by an end-to-end automation platform, brings innovative aspects in the RAN domain. It means that tasks as those above can be realized outside the traditional node context. Working on their own or together with other rAPPs, they can create improvements across all network domains.

In summary, SMO and the next generation of management and orchestration is essential to cope with the rising complexity of data and network systems, achieving not only a more manageable end-to-end network but also achieving the business propositions sought by operators: increased QoE and time to market for services creation and rapid innovation to achieve the full 5G potential.

Ericsson is exploring the possibilities
Ericsson views the rapidly progressing automation capabilities as essential to maintaining operational efficiency while also maximizing the business potential of the increasingly sophisticated 5G networks. Building on our leadership in RAN, core, management and orchestration systems, we bring a unique experience in delivering end-to-end 5G networks and OSS solutions (management and orchestration) to manage the smooth introduction of open and interoperable cloud-native RAN. In addition, Ericsson brings the following key capabilities in its automation platform that is driven by management and orchestration systems:

A programmable network with intent based AI/ML capabilities in management and orchestration solution to drive efficiency and automation.
A management and orchestration system that manages the PNF, VNF and CNF that comes with the introduction of cloud-native RAN.
Ability to develop unique use cases, onboard new applications in the management systems and rapid innovation by enabling an open app ecosystem for customers and partners.
An intelligent management and orchestration system that can provide network and service flexibility to support various new business models and use cases.
Ericsson is exploring the possibilities offered by SMO architecture to create an attractive solution for next-generation management and orchestration and accelerate the automation journey together with our customers.

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.

The Growing Intersection of AI and High-Performance Computing

Artificial intelligence is no longer a research curiosity—it is deeply embedded in everyday business operations. From natural-language chatbots in customer service centers to autonomous vehicles in logistics and distribution, AI-driven capabilities are reshaping how organizations function at every level.

This rapid adoption has created an equally rapid surge in demand for high-performance computing (HPC). Training deep learning models, running real-time AI inferencing, and performing analytics on massive datasets all require enormous computational resources—resources that traditional enterprise infrastructure was never designed to provide at scale.

HPC-Enabled AI: A Fast-Growing Market Segment

According to Hyperion Research, workloads including machine learning, deep learning, inferencing, training, graph analytics, and high-performance data analytics (HPDA) have all experienced robust growth in both budget allocations and organizational focus. Hyperion projected that HPC-enabled AI would grow at more than 30 percent annually to reach a $3.5 billion market by 2024.

Hyperion also noted a structural shift in where these workloads actually run. The percentage of HPC workloads executing in the cloud reached approximately 20% in 2019, a significant jump that reflects changing infrastructure priorities across the industry.

Why HPC Workloads Are Moving to the Cloud

Several converging factors explain why cloud environments are becoming a viable—and sometimes preferred—platform for HPC and AI workloads:

• Hybrid and multi-cloud maturity: Cloud service providers (CSPs) now offer hybrid and multi-cloud options specifically designed to give HPC users a near-seamless computing environment that spans on-premises hardware and cloud resources. This reduces the friction historically associated with migrating HPC jobs off dedicated clusters.
• Elastic capacity for complex workloads: As HPC applications grow in complexity—particularly when new AI techniques are layered onto traditional modeling and simulation workflows—the ability to burst into the cloud provides a more flexible and responsive computing environment without requiring permanent capital investment in hardware.
• Unified management across providers: Multi-cloud capabilities allow HPC users to manage resources from multiple CSPs through a single management plane. This is especially attractive to global enterprises that rely on different cloud providers in different geographic regions for latency, compliance, or cost reasons.
• Data gravity: For some AI workflows, the training data already resides in a cloud environment—collected from IoT sensors or assembled from multiple cloud-hosted datasets. Moving that data to an on-premises cluster can be expensive and slow. Running the workload where the data lives is often more practical.

The Role of HPC Expertise in Cloud Adoption

One barrier to cloud adoption for HPC teams has historically been the knowledge gap: HPC practitioners are experts in parallel computing and job scheduling, but may be less familiar with cloud instance selection, network topology, and cost optimization. Recognizing this, many CSPs now offer dedicated HPC support teams, in-house experts, and professional services to help users select the right instance types and configurations for specific workload profiles.

Hardware and software vendors have also built dedicated facilities to close this gap. Hyperion Research noted Dell’s HPC & AI Innovation Lab as one example of an environment that combines purpose-built hardware and software infrastructure with a staff of engineers, computer scientists, and domain experts engaged in the HPC community. The goal is to help end users understand best practices for solving HPC and AI problems, whether they are running AI alongside traditional simulation workloads or exploring entirely new application domains.

What This Means for DevOps and Platform Engineering Teams

For DevOps and platform engineering teams, the convergence of AI, HPC, and cloud infrastructure introduces new operational requirements:

• GPU and accelerator orchestration: AI training jobs typically require GPU instances. Teams need pipelines that can provision, schedule, and tear down GPU nodes efficiently—tools like Kubernetes with GPU operator support or cloud-native batch services become important.
• Cost management at scale: HPC workloads can consume enormous amounts of compute in short bursts. Spot or preemptible instances, combined with checkpointing strategies, are common approaches to managing cost without sacrificing throughput.
• Data pipeline reliability: When data gravity drives workload placement, robust, observable data pipelines between storage systems and compute clusters become a critical infrastructure concern.
• Hybrid connectivity: Organizations that retain on-premises HPC clusters while bursting to the cloud need low-latency, high-bandwidth connectivity—typically through dedicated interconnects or SD-WAN solutions—to avoid bottlenecks.

Key Takeaways

The movement of HPC workloads into cloud environments is being driven by the computational demands of AI, the maturity of hybrid cloud offerings, and the practical reality of data gravity. As CSPs invest more heavily in HPC-specific support and tooling, the barrier to running demanding AI and analytics workloads in the cloud continues to drop. For engineering teams, this shift means adapting infrastructure practices to support elastic, accelerator-driven, data-intensive workloads—while maintaining the cost controls and observability that production environments require.

“We’ve had to turn on the Black Friday/Cyber Monday protocol to support those traffic demands and surges so that they can handle whether people are hoarding and pulling forward their buying or just kind of changing their consumption behavior,” said Carrie Tharp, Google Cloud’s vice president of retail and consumer.

Google Cloud has ramped up capabilities for its grocery and mass merchandising customers to Black Friday and Cyber Monday levels as they face unparalleled demand for groceries and other essentials amid the coronavirus (COVID-19) pandemic.

Customers including Target are seeing an “unprecedented” period of demand that rivals the biggest holiday shopping days of the year, according to Carrie Tharp, Google Cloud’s vice president of retail and consumer.

“We’ve had to turn on the Black Friday/Cyber Monday protocol to support those traffic demands and surges so that they can handle whether people are hoarding and pulling forward their buying or just kind of changing their consumption behavior,” said Tharp, who served as chief digital and marketing officer at Dallas luxury retailer Neiman Marcus Group before joining Google Cloud last August. “What we’ve really focused on…is ensuring that we get all of our capabilities that can be used in short order — whether you were a customer previously or not — to the forefront for retailers to make sure they’re supporting their employees and their customers in this time.”

In a “normal” retail year, Google Cloud’s special Black Friday/Cyber Monday program helps customers with capacity planning and management to prevent outages or customer experience disruptions when the traditional start of the busy holiday shopping season begins on Thanksgiving or the day after. Google Cloud engineers typically work shoulder to shoulder with retail customers’ IT teams to ensure their e-commerce systems have the scalability and reliability they need in that peak season, according to Tharp.

“We basically took that program and turned it on for this period of time for the grocers and the mass merchants who are really seeing rapid acceleration in their online traffic and lots of new customer acquisition,” Tharp said. “A lot of our grocers are having hundreds of thousands of new customer acquisitions of people who didn’t previously shop with them or perhaps ate out more predominantly, and so we’re making sure that all of their systems are able to bear those traffic surges they’re seeing now.”

Retail is one of six industries that Google Cloud has been targeting with industry-specific solutions since Thomas Kurian assumed CEO responsibilities last year. Its retail customers are tapping its vertical retail solutions focused on accelerating omni-channel sales and using data to help drive business and operational improvements, according to Tharp.

“We’ve taken the best of our tools that we have and are using all of our account resources, our customer engineers, our professional services, to do a lot of these rapid development, rapid migrations as needed,” Tharp said. “Things like our Contact Center AI have become very important, where we’re able to do rapid stand-ups of virtual agents to help some of these partners with pretty basic (customer) questions that are just overwhelming their call centers and their stores about what are your hours, do you have certain products in supply, trying to use the buy online, pick up at curbside (options).”

Google Cloud had to support Canadian supermarket chain Loblaws with extra capacity because the number of its consumers using its express grocery pickup tool has rapidly accelerated.

Helping Non-Essential Retailers

The surge for some retailers comes as other non-essential chains have seen a rapid decline in demand and been forced to temporarily shutter their non-essential businesses, and while some consumers are curtailing spending on discretionary items amid the shelter-in-place/stay-at-home edicts and the economic fallout of widespread job layoffs and furloughs due to the coronavirus.

“Holistically, across both sets of retailers, we’ve very much focused on generally what I call demand forecasting,” Tharp said, noting a focus on basic predictive analytics. “We’re living in a very uncertain time. We’re helping a lot of our customers use public data sources like some of the public reporting of coronavirus cases and different shelter-in-place orders to create behavioral models to try to understand where should they be shifting inventory, how should they be thinking about replenishment.”

Sixty-one percent of brick-and-mortar retail U.S. stores – 259,851 locations with combined store and online sales of $168 billion in April 2019 – currently are closed due to the coronavirus pandemic, according to GlobalData, a data analytics and consulting company. The figures exclude gasoline, foodservice, and auto sales establishments. GlobalData expects retail sales to decline 31.4 percent this month from April 2019.

“Online isn’t making up for all of the demand that they would typically experience in-store, so there we’re very much focused on helping them with cost management, managing the ramp-down of certain services that they’re not using as much, because cloud allows for a much more flexible cost baseline for those retailers to be operating on,” Tharp said.

Retailers may have to deal with potentially changed shopping dynamics when stores are allowed to reopen and consumer spending returns to more normal levels. Prior to the coronavirus pandemic, the over-stored industry already had been seeing retailers reducing their physical footprints as consumers were shifting more of their spending online.

The coronavirus pandemic likely will spur record store closures this year and potential retail consolidation, and retail bankruptcies are likely to spike, according to research analyst Ken Perkins of Retail Metrics. Neiman Marcus Group is preparing to file for bankruptcy protection as early as this week, and Lord & Taylor and J.C. Penney also are exploring bankruptcies, according to published reports.

“Every day there seems to be a stream of temporary store closure extensions, employee furloughs, executive salary reductions, tapping of revolving credit facilities, capital expenditure cuts, share buyback and dividend postponements or cancellations, rent forbearance or refusals to pay, guidance withdrawals and scrapping of new store openings,” Perkins said in a recent retail report. “Sadly, if store closures persist for some time, many are likely to become permanent, while a spate of bankruptcy filings from weaker players is not out of the realm of possibility.”

Google Cloud already is helping its retail customers predict what their store reopening ramp-ups could look like, according to Tharp. For the retailers that are quite busy right now, that includes how to keep that supply chain running efficiently, Tharp said. And for retailers that have ramped down, it includes when and how does business start picking back up and how they can ensure they’re prepared.

“The coronavirus crisis created a new version of crisis for retail,” Tharp said. “Typically, as a retail executive, you have scenarios that plan for sections of a country being shut down or certain nodes being shut down, perhaps from a natural disaster, and you could really adjust…around it. This dynamic has really created a different world, where essentially all of your stores are shut down, and you may not be able to operate some warehouses. It’s created a renewed focus on business continuity, and cloud capabilities are an example of a big part of that conversation. Rather than being worried about accessing IT resources and business resources on-premise, having a partner like Google who can really handle those things seamlessly (allows you to) be focused on your customer experience.”

Google Cloud’s first pillar of its retail strategy — helping retailers accelerate digital and omni-channel revenue growth and be less store-dependent, if needed – speaks to the need for retailers to consider store footprint adjustments, according to Tharp.

“We see all of that being very important coming out of this (coronavirus) crisis,” she said. “We also have a lot of capabilities to help rapidly reduce costs. As retailers are in a liquidity crisis…they’ll be looking through their entire P&L (profit and loss statement) and trying to understand where they can drive cost out so they can continue to focus on their core value proposition with the customer. That’s where migration to the cloud, rethinking your martech (marketing technology) stack…can help reduce costs, and things like our contact center capability…can help improve the efficiency and cost baseline there. We expect, coming out of the crisis, a lot of deep, more accelerated conversations on these capabilities that were relevant before but are now much more urgent to help shore up the retail P&L coming out of a very difficult period.”

Kubernetes Brings More Data to the Table — but That Complicates Observability

A key driver of this complexity is the rapid adoption of Kubernetes. Surveys conducted around 2019–2020 pointed to Kubernetes becoming the dominant container orchestration platform, with the majority of enterprises either already running it in production or planning to do so within the year. The appeal is straightforward: Kubernetes gives development teams the flexibility to release services, features, and applications faster than traditional deployment models allow.

Faster delivery does not automatically mean higher-quality delivery, however. To capture the full value of Kubernetes, teams also need to adopt cloud-native architectural patterns, embed quality gates into the delivery pipeline, use modern deployment strategies such as canary releases or feature toggles, and maintain short, meaningful feedback loops from production back to engineering.

Kubernetes itself offers solid built-in visibility at the node and pod level, including health-check mechanisms and — through service meshes — live dependency data between services. In theory, this wealth of data should give organizations the insight they need to understand whether they are releasing better value faster. In practice, more data does not automatically produce more visibility. Large Kubernetes environments can contain literally billions of interdependencies spanning applications, containers, microservices, and cloud providers. No human team can track that volume of relationships manually, and relying on traditional, threshold-based monitoring approaches fails to capture the signal buried in the noise.

Full-Stack Observability Requires Deterministic AI

Getting full value from Kubernetes means achieving observability across the entire technology stack — not just individual nodes or pods, but the billions of interdependencies between every component. That scope makes AI assistance not a luxury but a practical necessity. The specific capability needed is a deterministic AI engine: one that applies well-defined algorithms to detect anomalies and trace their root causes through a continuously updated dependency map of the environment.

When a deterministic AI engine ingests Kubernetes events — state changes, workload shifts, resource allocation updates — it builds and maintains a real-time topology of the stack. This enables capabilities that traditional metrics and dashboards cannot match:

• Automatic instrumentation and mapping of containers as they are created, scaled, or destroyed.
• Dynamic resource management that reflects the current state of the cluster rather than static thresholds.
• Deep runtime inspection that correlates container-level events with broader service behavior.
• Real-time root-cause identification for degradations in service availability, performance, or user experience — surfaced in seconds rather than hours.

Because the AI resolves issues at the source before they propagate to end users, and feeds intelligence back into DevOps release pipelines, the result is a measurable acceleration of both deployment speed and quality. Teams previously bogged down in manual incident triage are freed to focus on work that creates business value.

AI-Powered Observability Depends on Quality Data and Consistent Monitoring

A deterministic AI engine is only as reliable as the data it ingests. If different teams inside a large organization bring their own preferred monitoring tools into a Kubernetes cluster, the data fed to the AI becomes inconsistent. Gaps, format mismatches, and coverage blind spots degrade the accuracy of dependency mapping and anomaly detection.

The practical solution is to establish a single, standardized monitoring layer that functions as a self-service capability across all Kubernetes clusters. This unified layer should:

• Support the full diversity of technologies introduced into the stack (multiple languages, frameworks, and cloud providers).
• Produce a consistent baseline of system and event data that developers, platform engineers, and the AI engine all draw from.
• Scale automatically as clusters grow, without requiring manual instrumentation for each new workload.

Consistent, high-quality input data makes the AI engine progressively smarter. A smarter engine delivers more precise observability, which in turn supports faster and more confident deployments.

Building a Positive Feedback Loop Between Kubernetes, Deterministic AI, and Observability

Kubernetes and deterministic AI are a natural pairing. Kubernetes provides the platform for orchestrating and instrumenting containerized workloads; the deterministic AI provides the intelligence layer for mapping interdependencies, detecting degradations, and resolving issues before they reach users. The relationship is self-reinforcing:

1. Kubernetes generates rich operational data as workloads run and evolve.
2. The deterministic AI ingests that data, builds a continuous dependency topology, and identifies anomalies with root-cause context.
3. Insights from the AI feed back into release pipelines and cluster configuration, improving the quality of the next deployment cycle.
4. Better deployments produce cleaner operational data, which further sharpens the AI’s models.

This feedback loop replaces the fragility of manual monitoring with a system that becomes more capable as the environment grows — exactly the opposite of the traditional model, where complexity outpaces human capacity. The end result is faster deployments, higher reliability, better user experiences, and more time for engineering teams to work on problems that matter.

Key Takeaways

• Kubernetes adoption has made container orchestration the default model for enterprise application delivery, but it also introduces billions of runtime interdependencies that exceed manual monitoring capacity.
• Deterministic AI — applying specific algorithms to a continuously updated dependency map — is the practical mechanism for achieving full-stack observability at Kubernetes scale.
• The accuracy and value of AI-powered observability is directly proportional to the consistency and quality of the monitoring data it receives; fragmented tooling undermines the AI’s effectiveness.
• A unified monitoring layer feeding a deterministic AI engine creates a self-reinforcing loop: better data produces smarter AI, which produces better observability, which supports better deployments.

Cloud Computing as a Pandemic Response Tool

The COVID-19 outbreak forced organisations across every sector to rethink how they operate almost overnight. Offices emptied, supply chains became unpredictable, and healthcare systems faced data demands at an unprecedented scale. Cloud computing — already deeply embedded in enterprise IT — became one of the most important enabling technologies during this crisis. This article examines the specific ways cloud infrastructure is supporting pandemic response efforts, from healthcare data management to remote workforce continuity.

Big Data and Health Surveillance

Epidemiological response depends on data: case counts, geographic spread, transmission rates, hospital capacity. Collecting and processing this data at a global scale requires storage and compute resources that few individual organisations can provision on their own. Cloud platforms address this directly by offering scalable, cost-efficient infrastructure for ingesting, processing, analysing, and sharing large datasets — a capability often referred to as data interoperability.

Without cloud infrastructure, the cost of building and maintaining the servers required to handle healthcare data at this scale would be prohibitive for most organisations. Cloud platforms allow healthcare providers, research institutions, and public health agencies to access enterprise-grade compute and storage on a pay-as-you-go basis, eliminating the need for large upfront capital expenditure.

AI-Powered Outbreak Detection: The BlueDot Example

One widely cited example of cloud-enabled pandemic response is BlueDot, a Canadian health intelligence company. According to public reporting, BlueDot’s platform issued alerts about unusual pneumonia cases in Wuhan in late December 2019 — several weeks before the World Health Organization’s public announcement and before the Chinese government announced travel restrictions.

BlueDot’s approach illustrates several cloud-native capabilities working together:

• Unstructured data ingestion: The platform ingests data from news articles, airline ticketing systems, and other open sources at scale.
• Natural language processing: AI models parse and classify this unstructured content across dozens of languages.
• Predictive modelling: By overlaying case data with commercial flight schedules, the system can model likely geographic spread patterns and issue early warnings to relevant authorities.
• Scalable infrastructure: Processing this volume of diverse, real-time data requires elastic compute resources that cloud platforms are well-suited to provide.

This kind of platform would be impractical to build and operate on on-premise infrastructure alone, particularly for a small organisation needing to scale rapidly during a crisis.

Supporting Remote Work at Scale

When governments issued stay-at-home orders and companies sent employees home, the immediate challenge was ensuring workers could still access the applications, data, and collaboration tools they needed to do their jobs. Cloud computing addresses this in several practical ways:

Application Access Without VPN Bottlenecks

Organisations that have already migrated line-of-business applications to cloud platforms — whether SaaS products or applications hosted on AWS, Azure, or GCP — can give employees access via a browser without requiring them to connect through a corporate VPN. This reduces the performance and capacity constraints that many organisations hit when remote VPN usage spikes suddenly.

Collaboration and Communication

Cloud-based collaboration tools — document co-editing, project management platforms, video conferencing — allow distributed teams to work together on shared assets in real time regardless of physical location. These tools offload compute-intensive tasks (such as video encoding) to cloud servers, reducing strain on end-user devices and home network connections.

Elastic Capacity for Unpredictable Demand

Organisations running customer-facing services on cloud infrastructure can respond automatically to traffic spikes by scaling out to additional compute instances across a provider’s global data centre network. When demand subsides, those instances can be terminated, allowing costs to scale down as well. This elasticity is particularly valuable when pandemic-related behaviour changes cause sudden, unpredictable shifts in service demand — for example, a dramatic increase in usage of telehealth portals or e-commerce platforms.

Cloud Adoption Challenges in Healthcare

Despite the clear advantages, cloud adoption in the healthcare sector has historically lagged behind other industries. Concerns around data privacy regulations (such as HIPAA in the United States and GDPR in Europe), data sovereignty, and legacy system integration have slowed migration for many healthcare organisations. The COVID-19 crisis has intensified the pressure to accelerate cloud adoption, but technology decisions made under emergency conditions still need to account for compliance requirements.

Organisations that do successfully move to cloud infrastructure can expect to participate more readily in shared data initiatives, leverage shared AI and machine learning tooling, and respond more quickly to future public health emergencies.

Cost Management Considerations

One operational risk associated with rapid cloud scale-up during a crisis is cost control. Organisations tend to scale cloud usage upward quickly in response to demand but are slower to scale it back down once the immediate pressure eases. Cloud cost management — tracking utilisation, identifying idle resources, and right-sizing instances — becomes especially important when adoption has been accelerated under emergency conditions. FinOps practices and tooling from cloud providers and third-party vendors can help organisations maintain visibility into spend as their cloud footprint grows.

Key Takeaways for DevOps and Engineering Teams

• Invest in cloud-based collaboration and remote access infrastructure before the next disruption. Organisations that had already migrated to cloud platforms adapted far more quickly when remote work became mandatory.
• Design for elasticity. Auto-scaling policies and infrastructure-as-code allow teams to respond to demand spikes without manual intervention.
• Data interoperability matters in a crisis. Platforms that can ingest, process, and share data across organisational boundaries are essential for coordinated public health response.
• AI and ML have practical early-warning applications. BlueDot demonstrates that combining diverse unstructured data sources with machine learning can produce meaningful epidemiological signals at scale.
• Cloud cost governance should scale with cloud usage. Rapid adoption without corresponding FinOps discipline can result in significant wasted spend once the crisis pressure eases.

The COVID-19 pandemic has accelerated cloud adoption across sectors and demonstrated the technology’s value as critical infrastructure. For engineering teams, the lesson is clear: cloud-native architecture is not just a cost or performance optimisation — it is an organisational resilience capability.

VMware’s recent acquisition of AI-powered network analytics startup Nyansa strengthens its network monitoring and management capabilities, but the startup’s technology also beefs up VMware’s broader, strategic efforts to help customers automate IT operations, analysts say.

VMware said it was buying Nyansa in January. The Palo Alto, California-based startup is behind Voyance, a cloud-based network analytics system that provides network visibility and, using machine learning, detects problems and anomalies and recommends potential fixes to improve network and application performance.

Related: Why SD-WAN Is Taking Over Enterprise Networks

Six years in existence, Nyansa has made inroads into the network performance management market and attracted some big-name customers, including Uber, Walmart, Tesla, and Proctor and Gamble.

VMware executives said in a blog post that the software giant would pair Voyance with SD-WAN by VeloCloud, its software-defined WAN platform – and that the acquisition would ultimately help it deliver on the promise of self-healing networks. (VMware acquired VeloCloud in 2017.)

Related: Juniper Says Its Bots are a Step Toward a “Self-Driving Network”

Analysts said acquiring Nyansa was a smart move by VMware, making it more attractive in the fast-growing SD-WAN market while also positioning it to better compete in the emerging AIOps (Artificial Intelligence for IT Operations) market, where analytics and machine learning automate IT operations.

“If you think about Nyansa as the platform to gain network visibility, and VMware as the management platform that can automate things happening on the network, you can see the power of them coming together: Nyansa can tell you if something is wrong, and VMware can go fix it,” said Brandon Butler, senior research analyst for enterprise networks at IDC.

VMware ranks second in market share behind Cisco in the burgeoning SD-WAN market, which IDC predicts will grow from $1.37 billion in 2018 to $5.25 billion in 2023. Other SD-WAN players include Silver Peak Systems, Nokia’s Nuage Networks, and Riverbed Technology.

End-to-End Network Visibility
Voyance has proven effective in monitoring and remediating problems on local area networks, including Wi-Fi networks, said Shamus McGillicuddy, a VP at market-research firm Enterprise Management Associates. For example, it measures user experience by inspecting network packets flowing in and out a Wi-Fi network, assigns scores to individual user connections, and then delivers an aggregate score to show overall health of the network.

Network visibility combined with machine learning-driven proactive anomaly detection not only improves network and application performance but also strengthens security, Butler said.

Nyansa has built up its network monitoring and analytics capabilities in the SD-WAN environment over the past year, Butler said.

VeloCloud already has some native network monitoring and analytics capabilities, McGillicuddy said. Integrating it with Nyansa’s technology will enable customers to monitor and manage their networks from campus and branch LANs to the WAN, he said. “VMware’s goal is to provide and end-to-end view.”

VMware also has a tool called vRealize Network Insight, which provides visibility into data center networks. While it hasn’t announced plans to integrate it with Voyance, the company could theoretically now create a single solution for managing all enterprise networks, including data center networks, he said.

Nyansa also aids VMWare in its drive toward automation, the analysts said. Voyance augments its existing automation tools, which include vRealize Automation for setting up and managing hybrid and multi-cloud environments.

“VMware has aspirations around AIOps-driven automation,” McGillicuddy said. “They want to enable their customers to automate a lot of things when it comes to troubleshooting and capacity management. And they see Nyansa as the brains behind a bigger automation future.”

Nadella Arrives in India for Three-Day Strategic Visit

Microsoft CEO Satya Nadella began a three-day visit to India in late February 2020, starting in Mumbai before moving on to meetings with Indian industry leaders, technology partners, and government officials. The visit was widely interpreted as a signal of Microsoft’s deepening commitment to the Indian market, particularly as the company accelerated growth of its Azure cloud platform across the Asia-Pacific region.

India ranks as one of Microsoft’s top-performing markets globally, with more than half of the company’s India revenue reported to be coming from cloud and cloud-based services at the time of the visit — a figure that underscores the strategic importance of the region as Microsoft competes with Amazon Web Services (AWS) and Google Cloud.

New Engineering Hub in Noida Anchors Microsoft’s India Expansion

Just prior to Nadella’s visit, Microsoft opened a new engineering hub in Noida, Uttar Pradesh — a facility intended to develop products and services that support digital innovation at a global scale. Analysts described the hub as a strategically important investment in Microsoft India’s long-term growth story.

“This is a strategic investment for Microsoft and will play an important role in Microsoft India’s expansion and growth story. It will also create thousands of jobs for Indian engineers.”

Sanchit Vir Gogia, Founder and CEO, Greyhound Research

Government Engagement: Digital Transformation of State Ecosystems

A central element of Nadella’s India itinerary was a planned meeting with Uttar Pradesh Chief Minister Yogi Adityanath to discuss the digital transformation of the state’s government ecosystem. This followed a broader initiative Microsoft had launched in August 2019, when the company organized a series of workshops in Lucknow aimed at equipping government IT officials with AI and cloud computing skills. The programme targeted training 5,000 government personnel over 12 months, with a focus on leveraging AI and secure cloud technology for more efficient and transparent governance.

Microsoft was empanelled as a public cloud service provider by India’s Ministry of Information Technology (MeitY) in late 2016 and has continued to hold certification as a cloud provider for multiple state governments. According to tech and policy advisor Prasanto K. Roy, Nadella’s engagement with state-level BJP leadership was part of a broader strategy to reinforce Microsoft’s relationship with both central and state governments.

“Nadella will want to assure the government of his and Microsoft’s complete support for government projects and policy, irrespective of his personal views. A deeper engagement with Uttar Pradesh and a meeting with CM Adityanath is part of that assurance and strategic engagement with both Centre and state governments.”

Prasanto K. Roy, Tech and Policy Advisor

Azure’s Growth and Microsoft’s Cloud Business Momentum

Nadella’s India visit came on the back of strong Azure performance. In its Q2 FY2020 earnings report, Microsoft disclosed that its Intelligent Cloud segment — which includes Azure — generated $11.9 billion in revenue for the quarter, representing 27% year-on-year growth.

Microsoft rebranded Windows Azure as Microsoft Azure in 2014 and has since built it into one of the world’s largest cloud infrastructure platforms, holding the position of second-largest cloud provider globally behind AWS. In India, enterprises such as InMobi and Myntra were reported to be moving to Azure as their preferred cloud platform.

Jayanth Kolla, Partner at Convergence Catalyst, noted that Microsoft had been expanding its research activities in India and running AI and deep-tech pilots in sectors including healthcare — areas where cloud computing and machine learning are increasingly seen as transformative tools.

“From a business point of view, Microsoft has been increasing its research activities in India and running a number of AI/deep tech pilots in India in areas like healthcare.”

Jayanth Kolla, Partner, Convergence Catalyst

Key Challenges: Data Localisation, Reliability, and Market Competition

Data Localisation Requirements

One of the most significant policy challenges Microsoft faced in India at the time of the visit was data localisation — a regulatory push requiring companies to store certain categories of data on servers physically located within India. For a cloud provider like Microsoft, complying with expanding localisation requirements would mean investing in additional data centres at both national and state levels, driving up capital expenditure and ongoing operational costs.

“This for a company like Microsoft that is running a cloud service like Azure and growing it would mean opening more and more data centres locally in India and at the state level too. This would increase the capex cost and the operational expenditure of the company.”

Jayanth Kolla, Partner, Convergence Catalyst

Service Reliability and CIO Concerns

Despite Azure’s growth momentum, enterprise customers in India expressed concerns that Microsoft still needed to address. According to Sanchit Vir Gogia of Greyhound Research, CIO feedback highlighted issues around Azure outages and support quality — specifically around cost transparency and response timeliness.

“Our CIO customers do often share with us their concerns with Azure outages and also support issues around costs and timeliness. Both are extremely important topics for organisations to ensure availability of resources in production.”

Sanchit Vir Gogia, Founder and CEO, Greyhound Research

A Competitive Cloud Market

In India, Microsoft competes directly with AWS and Google Cloud for enterprise workloads. While Microsoft had established a strong foothold and maintained decades-long relationships with the Indian government, the cloud market remained fiercely contested. Analysts noted that Microsoft had much to prove before it could claim cloud dominance in the Indian market, particularly as AWS continued to hold significant mind-share among large enterprises and startups alike.

Start-ups, Developers, and the Partnership Ecosystem

Beyond government and enterprise engagements, Microsoft’s India strategy at the time rested heavily on cultivating relationships with the start-up community and the developer ecosystem. India’s large pool of engineering talent and its rapidly growing start-up sector made it a natural fit for Microsoft’s cloud-first, AI-focused product strategy. Programmes targeting start-ups through Azure credits and AI tooling formed a complementary layer to the government-facing initiatives announced during the visit.

Context: Broader India-Tech Industry Dynamics

Microsoft’s visit took place against a backdrop of complicated dynamics between large technology companies and the Indian government. Several IT majors had scaled back bidding for government contracts citing delayed or non-payment issues. Wipro had, around the same period, suspended services related to Assam’s National Population Register (NPR) project. This environment made Microsoft’s careful diplomatic engagement with state and central government stakeholders all the more significant — maintaining trust while navigating politically sensitive terrain required measured communication at the executive level.

For Nadella, whose family roots are in India, the visit carried both personal significance and substantial business weight. As Microsoft continued to build out its Azure infrastructure globally and deepen AI integration across its product suite, India’s combination of engineering talent, a rapidly digitising government sector, and a large enterprise market made it a region where both the stakes and the opportunities were high.

What Is HeleCloud?

HeleCloud is an Amazon Web Services (AWS)-only systems integrator and managed services provider (MSP). The company operates offices in London and the Netherlands, with an engineering centre in Sofia, Bulgaria. Its business divides into two main practices: professional services and managed services.

According to CEO and co-founder Dob Todorov, the managed services arm runs as a “24/7 highly automated operation” designed to provide support for mission-critical applications on the AWS platform.

Who Does HeleCloud Work With?

HeleCloud’s customer base spans several sectors. Examples cited by Todorov include:

• CGI — the large systems integrator running the UK government’s smart metres programme, for whom HeleCloud builds and operates cloud-based infrastructure on AWS.
• Rakuten Viber — the voice-over-IP service, for whom HeleCloud reviews and optimises security and compliance on the AWS platform.
• Zopa — the financial services company, for whom HeleCloud is migrating significant portions of mission-critical systems from traditional hosting facilities to AWS.

The Core Misconception: AWS Does Not Manage Everything for You

The central message from Todorov is that many organisations adopted AWS under a fundamental misunderstanding of the shared responsibility model.

“Many organisations made the assumption that in AWS, it’s all managed for you from AWS and therefore you don’t need managed services at all — and that’s only partially true. It’s true that a lot of the conventional managed services provider work around hardware and connectivity is no longer required, but at the same time cloud requires someone to architect it to a fashion which is secure, compliant, high-performing and both cost and operationally efficient.”

— Dob Todorov, CEO, HeleCloud

In other words, AWS abstracts away physical infrastructure — servers, networking hardware, data-centre facilities — but it does not automatically design, secure, optimise or govern the workloads customers run on top of it. That responsibility still sits with the customer, or with an MSP acting on their behalf.

Where MSPs Add Value in the Cloud Era

Todorov argues that the role of the MSP has shifted rather than disappeared. Traditional MSPs focused on hardware racking, patching physical servers and managing network connectivity. On AWS, that lower layer is handled by Amazon. However, the layers above it — architecture, security posture, compliance, cost governance and operational automation — still require expert attention.

HeleCloud’s differentiation rests on two pillars:

• AWS specialism — deep, single-platform expertise covering ITIL v4 integration, infrastructure support, security and compliance management, performance management, service and data availability, and cost optimisation.
• DevOps as a managed service — the ability to deliver continuous integration and continuous delivery (CI/CD) pipelines as part of an ongoing managed engagement, rather than treating DevOps as a one-off professional services project.

“Managed services providers can add value by moving up the stack and making sure they automate and they involve DevOps principles on top of conventional IT service management.”

— Dob Todorov, CEO, HeleCloud

Todorov also points to the pace of AWS feature releases as a practical reason organisations need ongoing help. AWS regularly launches new services and updates existing ones; keeping an estate current and secure in that environment requires continuous engineering attention.

The Changing Cloud Market: New Build vs. Legacy Migration

Todorov describes a clear split in how organisations are approaching cloud adoption:

• Net-new workloads are almost universally being built in the public cloud.
• Existing legacy applications are largely staying in on-premises data centres or private cloud environments, at least for the time being.

“Cloud is becoming the mainstream platform for virtually all organisations; everything new is being built in the public cloud space, but a trend we’re not seeing is mass migration of existing applications — so even though there are some projects, for the time being large organisations are keeping their existing estate in data centres or in the private cloud.”

— Dob Todorov, CEO, HeleCloud

Todorov believes migrating existing applications to public cloud would deliver better security and compliance outcomes, but acknowledges that large organisations have not yet reached that conclusion at scale. Risk aversion, regulatory complexity and the sheer effort of re-platforming are common barriers.

Multi-Cloud Strategy

As multi-cloud becomes a common enterprise strategy, HeleCloud — being AWS-only — addresses non-AWS requirements by partnering with large systems integrators that have capabilities on other platforms. This allows the company to participate in broader deals without diluting its AWS depth.

What’s Next: SaaS, PaaS, AI and Data

Looking ahead, Todorov identified two trends he expected to define the following years (from the perspective of early 2020):

• Cloud providers moving further up the stack — expanding their SaaS and PaaS offerings beyond raw infrastructure, giving customers higher-level managed capabilities.
• Data and AI/ML convergence — organisations archiving large volumes of data on platforms such as AWS, and then applying artificial intelligence and machine learning tools to derive insight from that data.

Key Takeaway for DevOps and Cloud Engineers

The shared responsibility model is not just a compliance checkbox — it is an operational reality. AWS secures and maintains the underlying infrastructure, but every customer is responsible for what they build on top of it: architecture decisions, IAM policies, encryption configuration, patching schedules for managed services, cost guardrails and CI/CD pipelines. Whether that work is done in-house or by an MSP, it must be done. Organisations that skip it do not escape the responsibility; they simply accumulate unmanaged risk.

Why Rapid Prototyping Matters for AI Projects

Managers and technical staff who are new to artificial intelligence are often skeptical that a working prototype can be built in just one or two months. That skepticism is understandable — AI projects can feel abstract until something tangible exists. In practice, however, many rapid prototypes have been built in a matter of weeks. These prototypes are deliberately limited in scope; their purpose is not to satisfy all final system requirements but to prove a concept and open a productive dialogue between developers and stakeholders.

The Business Case for Building a Prototype First

Rapid prototyping delivers several concrete benefits before significant budget is committed:

• Feasibility validation. A working prototype — even a narrow one — confirms that the core technical approach is viable before the team invests heavily in full-scale development.
• Early requirements alignment. When both developers and end users are looking at the same screen output, it becomes far easier to resolve differences in problem scope, expected behavior, and performance requirements. Even a detailed 500-page specification cannot anticipate every ambiguity that surfaces during real use.
• Reduced misunderstanding. A physical demonstration replaces abstract descriptions, giving all parties a shared reference point. Misinterpretations that would otherwise surface late — and expensively — are caught early.
• Iterative refinement in uncertain domains. Many AI applications operate in problem spaces that are not fully understood, even by the domain experts who will use them. Because modifying a knowledge base in a prototype is relatively inexpensive, user thinking can be refined without causing serious schedule or cost overruns.

A Practical Example: From Concept to Demonstration in Two Weeks

Consider a scenario that illustrates how quickly a prototype can move from idea to demonstration. A group of development engineers and manufacturing personnel conceived of a knowledge-based system they believed would significantly improve the quality assurance function. When they brought the concept to their manager — who only vaguely understood what the system would do — she agreed it was worth exploring, but asked for something more concrete before approving any development budget.

“Before we talk about funding you for this effort, please define the scope of the prototype. Give me a better feel for what this system will do and how much will be required to develop it.”

Rather than writing a lengthy specification document, the development team went directly to the lab. Two weeks later, they invited the manager to see a working demonstration containing roughly 15 to 20 rules. In that short time, they had demonstrated both concept feasibility and tangible potential benefits — far more persuasively than any written document could have.

What a Typical AI Rapid Prototype Looks Like

While scope varies considerably, a typical rule-based AI rapid prototype contains somewhere between 50 and 100 rules and takes one to three months to build. The use of mature software tools and frameworks can shorten that timeline further.

It is important not to let enthusiasm for the prototype’s quick delivery create unrealistic expectations about the production system. A rapid prototype is intentionally constrained. Common limitations include:

1. Shallow knowledge base — insufficient depth for a full-scale production system.
2. Narrow test coverage — typically handles only one or two limited test cases or scenarios.
3. Inefficient implementation — performance is not optimized; the focus is on correctness and demonstration.
4. Constrained user interface — the UI is functional but not polished or production-ready.
5. Developer dependency — the prototype may require the developer to supervise or assist its operation in early stages.

Using the Prototype as a Platform for Growth

A well-scoped AI prototype does more than prove a single concept. When evaluated and used by domain experts and end users, it frequently sparks ideas for additional applications — some of which may go well beyond the original project scope. This is one reason it is worth selecting an initial AI project that can serve as a building block for future work.

In a DevOps context, this iterative approach maps naturally to continuous delivery practices. Treat the prototype as the first increment in a longer delivery pipeline: instrument it, gather feedback, and use that signal to prioritize the next increment of development. The goal is not a perfect first release but a learning loop that reduces risk at every stage.

Key Takeaways for AI Project Managers

• Do not wait for a complete specification before building something demonstrable. A working prototype, even with 15–20 rules, communicates more than 500 pages of documentation.
• Set clear expectations about prototype limitations before the demonstration. Stakeholder excitement is valuable, but must be calibrated against the work remaining.
• Choose AI projects that can grow. A prototype that serves as a foundation for multiple future applications delivers compounding value.
• Leverage DevOps practices — version control of the knowledge base, automated testing of rules, and iterative deployment — to keep the prototype maintainable as it evolves toward a production system.

Company Achieves Milestone Just Eight Months After Joining the AWS Partner Network, Proving Technical Validity and Customer Success of dotData’s AutoML 2.0 Platform

dotData, focused on delivering full-cycle data science automation and operationalization for the enterprise, announced that it has achieved Amazon Web Services (AWS) Machine Learning (ML) Competency status.

dotData achieved AWS ML Competency status only eight months after joining the AWS Partner Network (APN). The certification recognizes dotData as an APN Partner that accelerates the full-cycle ML and data science process and provides validation that dotData has deep expertise in artificial intelligence (AI) and ML on AWS and can deliver their organization’s solutions seamlessly on AWS. dotData achieved AWS ML Competency status through a rigorous qualification process that included a strict validation of their capabilities to demonstrate technical proficiency and proven customer success, as well as a technical audit of its dotData Enterprise solution.

dotData provides solutions designed to improve the productivity of data science projects, which traditionally require extensive manual effort from valuable and skilled resources, by automating the full life-cycle of the data science process, from business raw data through feature engineering to implementation of ML in production utilizing its proprietary AI technologies.

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dotData’s AI-powered feature engineering automatically applies data transformation, cleansing, normalization, aggregation, and combination, and transforms hundreds of tables with complex relationships and billions of rows into a single feature table, automating the most manual data science projects.

“By leveraging the ease of use of dotData Enterprise and the power of AWS, we were able to develop and deploy our first machine learning models in weeks, without the need for data scientists,” said Phillip Byrnes, Director of Business Intelligence, of US Electrical Services Inc. an independent electrical supply distributor headquartered in Middletown CT. “dotData Enterprise is like having a mathematician in a box, it helps us optimize our business by leveraging our existing Business Intelligence teams and resources. The product is that easy to use.”

“From its foundation, dotData’s vision has been to make AI and ML accessible to as many people in the enterprise as possible. We are proud of the successful experience of US Electrical Services’ BI team, which is a testament to our commitment to truly democratize AI in enterprise,” said Ryohei Fujimaki, founder and CEO of dotData. “Achieving AWS ML Competency status in just eight months recognizes our ability to deliver an outstanding product that dramatically accelerates the AI and ML initiatives of AWS users and maximizes their business impacts.”

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AWS ML Competency Partners provide solutions that help organizations solve their data challenges and enable ML and data science workflows. The program is designed to highlight APN Partners who have demonstrated technical proficiency in specialized solution areas and helps customers find the most qualified organizations with deep expertise and proven customer success.

dotData democratizes data science by enabling existing resources to perform data science tasks, making enterprise data science scalable and sustainable. dotData automates up to 100 percent of the data science workflow, enabling users to connect directly to their enterprise data sources to discover and evaluate millions of features from complex table structures and huge data sets with minimal user input. dotData is also designed to operationalize data science by producing both feature and ML scoring pipelines in production, which IT teams can then immediately integrate with business workflow. This can further automate the time-consuming and arduous process of maintaining the deployed pipeline to ensure repeatability as data changes over time. With the dotData GUI, the data science task becomes a five-minute operation, requiring neither significant data science experience nor SQL/Python/R coding.

The seven trends below were identified as major forces reshaping enterprise computing. While the original analysis was published in early 2020, most of these shifts have only accelerated and remain highly relevant to engineering teams today.

1. The Continued Rise of Hybrid Cloud

Enterprises rarely commit to a single cloud provider. Instead, they distribute workloads across multiple public clouds and on-premises infrastructure — a model known as hybrid or multi-cloud. Research firm Gartner predicted that by 2020, 75 percent of organizations would have deployed a multi-cloud or hybrid cloud model, driven by the need for flexibility, vendor independence, and regulatory compliance.

MarketsandMarkets projected the hybrid cloud market would grow from approximately $44.6 billion in 2018 to $97.6 billion by 2023, at a compound annual growth rate (CAGR) of around 17 percent. Key drivers include cost efficiency, scalability, operational agility, and the ability to keep sensitive workloads on-premises while shifting less critical services to public cloud providers.

For DevOps teams, hybrid cloud demands investment in unified management tooling, consistent identity and access controls across providers, and portable CI/CD pipelines that deploy reliably to any target environment.

2. Edge Computing Moves to Center Stage

The proliferation of IoT devices is generating data volumes that centralized cloud data centers struggle to absorb efficiently. Gartner forecast that 75 percent of enterprise-generated data would be created and processed outside a traditional centralized data center or cloud by 2025. An IDC FutureScape report projected that by 2022, 40 percent of enterprises would have doubled their IT asset spending in edge locations compared with core data centers.

Edge computing addresses this by moving compute and storage resources closer to where data is generated — factory floors, retail locations, vehicles, and remote facilities. Lower latency, reduced bandwidth costs, and improved resilience when connectivity to a central cloud is intermittent are the primary benefits.

For engineering teams, edge deployments introduce new operational challenges: managing a large fleet of distributed nodes, ensuring consistent software delivery, and maintaining security across potentially thousands of edge sites. Kubernetes distributions designed for resource-constrained environments (such as K3s) have emerged as a practical response to these challenges.

3. Hyperscale Data Centers Scale Up

Hyperscale refers to an IT architecture’s ability to scale compute, storage, and networking resources rapidly and proportionally in response to demand. Hyperscale data centers — operated by the largest cloud providers and technology companies — are designed from the ground up for elastic capacity.

MarketsandMarkets estimated the hyperscale data center market would grow from approximately $25 billion in 2017 to over $80 billion by 2022. Cisco predicted that by 2021, traffic within hyperscale data centers would quadruple and that hyperscale facilities would account for 55 percent of all data center traffic.

The modular design of hyperscale infrastructure means that individual components — storage nodes, compute blades, and network switches — can be added or replaced independently, giving operators precise control over capacity without large-scale forklift upgrades.

4. Disaster Recovery as a Service (DRaaS) Gains Momentum

Unplanned downtime is expensive. Gartner estimated the average cost of IT downtime at approximately $5,600 per minute, while IBM’s Cost of a Data Breach report put the average total cost of a breach at close to $3.92 million. Regulatory frameworks such as GDPR add a compliance dimension: organizations must demonstrate that they can recover systems and data within defined timeframes.

Disaster Recovery as a Service (DRaaS) addresses this by providing automated, cloud-based failover and recovery capabilities. Rather than maintaining expensive secondary data centers, organizations replicate workloads to a cloud provider and invoke recovery runbooks automatically. IDC projected the DRaaS market would reach $4.5 billion by 2020, growing at approximately 15 percent annually through 2023.

For DevOps practitioners, DRaaS fits naturally into infrastructure-as-code workflows: recovery environments can be defined declaratively, tested in isolation using chaos engineering practices, and spun up on demand rather than maintained continuously at full cost.

5. Hyperconverged Infrastructure (HCI) Sees Increased Adoption

Hyperconverged infrastructure (HCI) integrates compute, storage, and networking into a single software-defined platform managed through a unified interface. This approach eliminates much of the complexity associated with traditional three-tier data center architectures and reduces the operational burden on smaller IT teams.

Gartner predicted that by 2020, 20 percent of business-critical applications then running on three-tier infrastructure would migrate to HCI. Research firm Mordor Intelligence projected the HCI market would register a CAGR of over 13 percent from 2019 to 2024, driven by its simpler management model and lower total cost of ownership compared with disaggregated legacy infrastructure.

HCI is increasingly positioned as an on-premises alternative or complement to public cloud platforms, particularly for organizations with data sovereignty requirements or latency-sensitive workloads that cannot easily move off-premises.

6. AI Transforms Data Center Operations

Artificial intelligence is becoming an operational tool within data centers themselves, not only in the applications they host. AI-based systems can analyze historical telemetry to predict hardware failures before they occur, automatically rebalance workloads to optimize energy consumption, and flag anomalous network behavior that may indicate a security incident.

Gartner predicted that by 2020, 75 percent of organizations would experience visible business disruptions due to infrastructure and operations skill gaps — a significant increase from fewer than 20 percent in 2016. AI-driven automation is one response to this skills shortage, handling routine operational tasks and escalating only genuinely novel problems to human engineers.

Gartner also warned that data centers failing to prepare adequately for AI workloads risked becoming operationally or economically unviable. Modern GPU-dense infrastructure, high-bandwidth networking, and low-latency storage are prerequisites for serving AI inference and training at scale — requirements that differ substantially from traditional CPU-centric designs.

7. Containers Become the Standard Deployment Unit

Containers package application code together with its dependencies, making applications portable across development laptops, on-premises servers, and any public cloud. This “build once, run anywhere” property has made containers the default packaging format for microservices-based architectures.

Gartner predicted that by 2023, more than 70 percent of global organizations would be running more than two containerized applications in production, up from fewer than 20 percent in 2019. IDC projected that 95 percent of new microservices would be deployed in containers by 2021.

Container adoption is closely coupled with hybrid cloud growth: because containers abstract away underlying infrastructure differences, they simplify deployment across heterogeneous environments. Kubernetes has emerged as the dominant container orchestration platform, supported by all major cloud providers and a large ecosystem of tooling covering networking, storage, observability, and policy enforcement.

What These Trends Mean for Engineering Teams

These seven trends reinforce a consistent theme: enterprise IT infrastructure is becoming more distributed, more automated, and more software-defined. The boundaries between on-premises and cloud, between development and operations, and between application code and infrastructure configuration continue to blur.

• Platform engineers should invest in tooling that works consistently across hybrid and multi-cloud environments.
• Infrastructure teams should evaluate HCI and edge deployment models for workloads with latency or data-sovereignty constraints.
• SRE and operations teams should formalize DRaaS strategies and validate them regularly with automated failover tests.
• Developers should treat containers as the default packaging format and design services for portability from the outset.
• Leadership should plan for AI-driven automation to absorb routine operational tasks as the skills gap widens.

Cloud computing is not a destination but an ongoing architectural evolution. Teams that track these trends and build adaptable infrastructure will be better positioned to deliver reliable, scalable services regardless of how the technology landscape continues to shift.

Original analysis sourced from entrepreneur.com (2020). Market figures reflect analyst predictions available at the time of original publication.

It is no longer a surprise that the volume of enterprise data is growing rapidly. What is less widely discussed is that the origin of that data is shifting just as fast. An increasing share of data is now being generated in the cloud and at the network edge, not in traditional data centres. This shift forces engineering and operations teams to rethink where and how they analyse, understand, and manage data — ideally without routing everything through a centralised data centre as an intermediary.

Three areas are experiencing the most significant change when it comes to enterprise data strategy:

1. Edge Data Requires Edge Data Management

Edge computing buildout is already well underway, and its pace is accelerating. Key drivers include the Internet of Things (IoT), autonomous vehicles, and biometric systems — all of which generate large volumes of data far from centralised infrastructure. According to IDC forecasts cited in the original source, 50% of all enterprise IT infrastructure could be deployed at the edge by 2023, compared to roughly 10% at the time of writing.

This creates a straightforward operational challenge: if data is being generated at the edge, why move all of it to a data centre before you can do anything useful with it? The answer increasingly is that you should not. Edge data management addresses this by allowing teams to:

• Analyse data locally at the point of generation.
• Classify which data is high-value and needs to be sent back to the data centre or cloud for deeper processing.
• Process or discard lower-value data directly at the edge, reducing bandwidth costs and latency.

As more applications are purpose-built for edge deployment, the tooling around edge data management — including lightweight databases, stream processing, and local orchestration — will grow in maturity and adoption.

2. Multi-Cloud Adoption Makes Cross-Cloud Data Management a Requirement

Most large enterprises using public cloud services have already adopted some form of hybrid or multi-cloud strategy. Rather than committing to a single cloud provider, organisations are increasingly distributing workloads across two or more clouds — whether for cost optimisation, resilience, regulatory compliance, or to avoid vendor lock-in.

The operational consequence is data sprawl: copies of data, pipelines, and storage systems spread across AWS, Azure, Google Cloud, on-premises infrastructure, and potentially the edge. Managing this sprawl is genuinely difficult when each cloud provider offers its own native data tools with proprietary interfaces.

Cross-cloud data management platforms aim to solve this by providing a unified layer to discover, catalogue, govern, and move data regardless of where it physically resides. Key capabilities engineering teams should evaluate in such platforms include:

• Vendor-neutral connectivity: Integration with multiple storage and compute providers without requiring data to be normalised into a proprietary format.
• Unified metadata and cataloguing: A single view of what data exists, where it lives, and who owns it.
• Policy-based governance: Consistent access controls, retention policies, and compliance rules applied across all environments.
• Data portability: The ability to move or replicate data across clouds without manual re-engineering.

As multi-cloud becomes the default rather than the exception, solutions that are tightly coupled to a single cloud vendor’s ecosystem become a liability rather than an asset.

3. Analytics-Driven AI Moves from Buzzword to Operational Tool

Artificial Intelligence (AI) and Machine Learning (ML) have been prominent themes in enterprise technology conversations for several years. For much of that time, they occupied a space somewhere between genuine potential and marketing overstatement. That balance is shifting.

The application of AI to data management itself — rather than just to business analytics — is where some of the most practical near-term value lies. The core idea is straightforward: you cannot manage data you do not understand. By applying ML models to analyse data characteristics, usage patterns, access frequency, and content, data management platforms can make better automated decisions about storage tiering, archival, deletion, access control, and anomaly detection.

Concretely, analytics-driven AI in data management can enable:

• Intelligent data tiering: Automatically moving cold data to cheaper storage without manual intervention.
• Anomaly and threat detection: Identifying unusual data access patterns that may indicate a security incident.
• Data quality improvement: Flagging duplicate, incomplete, or inconsistent records across distributed repositories.
• Capacity forecasting: Predicting storage growth trends to inform infrastructure planning.

Data lakes spanning multiple storage repositories provide the breadth of signal needed to train and refine these models effectively. The practical output is a management layer that becomes smarter over time — reducing toil for operations teams while improving data hygiene across the organisation.

Bringing It Together: Manage Data Where It Lives

The common thread across all three of these trends is a shift away from the assumption that all data must eventually pass through a central data centre before it can be properly managed. That assumption made sense when almost all enterprise data was generated on-premises. It is increasingly untenable when data is being produced continuously at the edge, in multiple clouds, and across distributed application stacks.

Engineering and operations teams that get ahead of this shift will invest in:

• Edge-native data management tooling that reduces unnecessary data movement.
• Cloud-agnostic governance platforms that work across all environments without vendor lock-in.
• AI-assisted analytics that help teams understand and act on data at scale without proportionally growing headcount.

The organisations that treat data management as a first-class engineering concern — rather than an afterthought bolted on after architecture decisions are made — will be best positioned to derive real value from their data wherever it happens to live.

This article breaks down five technology trends that are expected to drive enterprise investment decisions in the near and medium term, and explains what each means in practice for engineering and DevOps teams.

1. Digital Transformation: Moving the Entire Business Online

Digital transformation remains the overarching strategic priority for most enterprises. The core idea is straightforward: migrate processes, customer touchpoints, and decision-making from analogue or legacy systems to digital platforms that are faster, more data-driven, and easier to scale.

Industry surveys have consistently found that a large majority of senior executives view digital transformation as essential for survival, yet an equally significant portion acknowledge they are behind where they need to be. The gap between intention and execution is the defining challenge. Companies that close it gain measurable advantages in speed-to-market, customer experience, and operational cost. Those that do not risk becoming structurally uncompetitive.

For engineering organisations, digital transformation translates into concrete decisions: modernising monolithic applications, adopting cloud-native architectures, implementing continuous integration and continuous delivery (CI/CD) pipelines, and retiring technical debt systematically rather than deferring it indefinitely.

2. IoT: Managing Thousands of New Attack Surfaces and Data Sources

The Internet of Things extends computing and connectivity to physical devices — sensors on factory floors, smart meters in buildings, medical monitoring equipment, and logistics trackers. The volume of data these devices generate creates powerful opportunities for real-time operational intelligence.

However, enterprise adoption at scale still faces three concrete barriers:

• Cost: Deploying, managing, and maintaining large device fleets requires significant capital and operational expenditure, particularly when retrofitting existing infrastructure.
• Security: Each connected device is a potential entry point. Many IoT devices ship with weak default credentials, infrequent firmware updates, and limited compute resources for on-device security controls. This dramatically expands the attack surface organisations must monitor.
• Integration complexity: Connecting device data to existing enterprise systems — ERP, CRM, analytics platforms — requires robust data pipelines and governance frameworks that many organisations are still building.

For DevOps teams, IoT adoption means owning firmware CI/CD pipelines, device fleet management, and integration with cloud IoT platforms. Security must be designed in from the start, not bolted on after deployment.

3. AI and Machine Learning: Moving from Hype to Operational Reality

Artificial intelligence and machine learning have passed the peak of inflated expectations and are now delivering measurable value in specific, well-scoped use cases. The most common enterprise applications include:

• Business intelligence and forecasting: ML models trained on historical data can surface demand patterns, churn signals, and anomalies that rule-based systems miss.
• Process automation: Repetitive, rules-adjacent tasks — document classification, ticket routing, invoice processing — are strong candidates for ML-driven automation.
• AI-assisted decision making: Organisations are embedding model outputs into operational workflows so that human decision-makers have data-driven recommendations at the point of action.
• Digital identity verification: Computer vision and natural language processing are increasingly used to verify identities for online financial services and onboarding workflows.

Deep learning, the subset of ML that powers most modern AI breakthroughs, requires significant compute and large labelled datasets. This makes it better suited to organisations with mature data infrastructure. For teams earlier in the ML journey, classical ML methods often deliver better ROI faster.

From a DevOps perspective, AI/ML introduces a distinct operational discipline — MLOps — covering model versioning, data pipeline reliability, model monitoring in production, and retraining cadences. These are not software engineering problems that existing toolchains solve out of the box.

4. Quantum Computing: A Long-Term Research Bet

Quantum computing leverages the principles of quantum mechanics — superposition and entanglement — to perform certain categories of computation exponentially faster than classical hardware. Problems that involve searching vast solution spaces, simulating molecular interactions, or optimising complex logistics networks are candidates for quantum speedup.

However, quantum computing remains predominantly in the research and development phase. Current quantum hardware is noisy, error-prone, and requires extreme environmental conditions to operate. Practical, fault-tolerant quantum computers capable of running production workloads do not yet exist at commercial scale.

Major technology companies and national research agencies are investing heavily in quantum R&D, and cloud providers have begun offering quantum computing access as a service, allowing enterprises to experiment without building dedicated hardware. For most organisations, the near-term opportunity is in developing quantum literacy — understanding the problem classes where quantum advantage is theoretically achievable — rather than deploying production quantum workloads.

One area where quantum computing has immediate strategic relevance is cryptography. Sufficiently powerful quantum computers could break widely used public-key encryption schemes. Security teams should be tracking post-quantum cryptography standards development, notably the work underway at NIST, and planning migration timelines accordingly.

5. DevOps: Evolving Toward Customer-Centric Delivery

DevOps adoption has matured significantly, but many organisations are hitting a ceiling. CI/CD pipelines are in place, but release velocity is not translating directly into improved customer outcomes. The next evolution of DevOps practice is shifting focus from speed for its own sake to outcomes — specifically, customer value delivered per release cycle.

Key areas of development in DevOps practice include:

• Platform engineering: Building internal developer platforms (IDPs) that abstract infrastructure complexity and let product engineers focus on feature delivery rather than operational toil.
• Continuous testing evolution: Moving beyond functional regression testing toward contract testing, chaos engineering, and continuous performance validation in production-like environments.
• Developer experience: Reducing cognitive load and friction in development workflows to maintain sustainable pace without burning out engineering teams.
• Security integration (DevSecOps): Embedding security scanning, policy enforcement, and compliance checks directly into pipelines rather than treating them as release gates.

Teams experiencing stress from deployment pressure should treat that signal seriously. Sustainable delivery velocity requires good observability, a culture of blameless incident review, and a genuine commitment to reducing toil through automation — not simply demanding more from engineers.

Where to Focus Investment

For most enterprises, the practical investment priority follows a logical sequence. Digital transformation and DevOps maturity are foundational — without them, investments in AI and IoT will underdeliver because the organisational and technical infrastructure to operationalise those technologies is not in place. AI and IoT deliver compounding returns on top of a solid digital and delivery foundation. Quantum computing is a longer-horizon bet best approached through education and selective experimentation today.

Across all five areas, cybersecurity is a cross-cutting concern rather than a separate line item. Every new technology surface — IoT devices, ML model APIs, quantum-vulnerable encryption — requires a security posture that scales with adoption.

Original analysis sourced from reporting by TechRepublic.

What Is Industry 4.0?

The term Industry 4.0 — or the Fourth Industrial Revolution — describes the current era of digitally driven industrial transformation. To understand why it matters, a brief look at history helps:

• First Industrial Revolution (late 18th century): Water and steam power enabled mechanised production, fundamentally changing manufacturing and labour.
• Second Industrial Revolution: The rise of mass production lines and electrification accelerated output at scale.
• Third Industrial Revolution (20th century): Information technology and early automation began replacing manual processes in factories and offices.
• Fourth Industrial Revolution (present): The convergence of the Internet of Things (IoT), artificial intelligence, robotics, big data, and cloud computing is reshaping entire industries — not just manufacturing, but also finance, healthcare, logistics, and more.

Unlike previous revolutions that were largely driven by physical machinery, Industry 4.0 is fundamentally a data and connectivity revolution. The ability to collect, transmit, store, and act on vast amounts of data in real time is its defining characteristic — and that is precisely where cloud computing plays a central role.

Cloud Computing as the Backbone of Industry 4.0

Cloud computing provides the infrastructure and services that make Industry 4.0 technologies practical at scale. IoT devices generate enormous volumes of sensor data; AI and machine-learning workloads require flexible compute capacity; robotic automation systems need low-latency connectivity and high availability. Cloud platforms address all of these requirements in a way that on-premises infrastructure alone cannot efficiently deliver.

An Oracle report titled Cloud: Opening Up the Road to Industry 4.0 surveyed 1,200 technology decision-makers across EMEA in mid-size and large companies. According to the report, 60 percent of respondents said an integrated cloud approach would unlock the potential of disruptive technologies such as robotics and artificial intelligence. The key insight is that compute services tightly integrated with a cloud platform — rather than isolated cloud storage or SaaS tools — are what enable genuinely innovative and disruptive applications in an Industry 4.0 context.

Why the BFSI Sector Is a Leading Cloud Adopter

The Banking, Financial Services, and Insurance (BFSI) sector has been at the forefront of cloud adoption — and for good reason. Financial institutions process enormous transaction volumes, operate under strict regulatory requirements, and face relentless pressure to reduce costs and improve customer experience. Cloud computing addresses all three simultaneously.

Research cited from Harvard Business Review Analytic Services indicates that 74 percent of businesses believe cloud computing has given them a competitive advantage. In the financial services context, this advantage manifests as faster time-to-market for digital products, better fraud detection through real-time analytics, and the ability to scale payment infrastructure during peak demand without over-provisioning hardware.

Diwakar Nigam, Managing Director of Newgen Software, has highlighted that the banking sector’s cloud adoption is driven by four core advantages: scalability, lower capital costs, ease of operations, and resilience. Newgen’s own Cloud/SaaS business reportedly achieved a five-year CAGR of 127 percent at the time of reporting — a figure that reflects broad industry demand rather than being unique to one vendor.

T V Ramanmurthy, General Manager of IT at Bank of Maharashtra, offered a candid perspective on adoption pace: when BHIM and the Unified Payments Interface (UPI) were implemented by the National Payments Corporation of India (NPCI) in 2017, it reportedly took six months before the institution fully recognised how critical cloud infrastructure was to those payment systems. That delay illustrates a common pattern — cloud’s value in financial services is often understood retrospectively, after a scaling event exposes the limits of on-premises systems.

Cloud Adoption Trends in India’s Financial Sector

India represents a particularly important market for cloud-driven Industry 4.0 transformation. NASSCOM data cited in the original reporting projects the Indian cloud market to reach $7.1 billion by 2022. A separate study by enterprise cloud firm Nutanix suggested India was on track to lead the world in hybrid cloud adoption, with hybrid cloud workloads expected to more than triple — from 13 percent in 2018 to 43 percent within 24 months of that report.

IDC projections referenced in the source material suggest that while traditional software would continue to grow (at approximately 11 percent), cloud infrastructure and cloud-based applications were forecast to grow significantly faster — underscoring the structural shift in how enterprise IT budgets are allocated.

“Each and every financial institution in India has its own perception pertaining to leveraging cloud technology in their institution completely. In reference to the current level of digitisation in the country, it is evident to make use of tech-driven initiatives and cloud is definitely going to play a major role in the transformation.”

— Zulkernain Kanjariwala, Head of IT, Doha Bank

Key Benefits of Cloud Computing for Banking and Finance

Cost Efficiency

Traditional banking IT requires significant capital expenditure (CapEx) on data centres, hardware procurement, and maintenance contracts. Cloud computing converts much of this into predictable operational expenditure (OpEx) on a consumption basis. Banks can redirect capital toward core banking product development rather than infrastructure management — a meaningful competitive advantage in a sector where margins are under constant pressure.

Reliability and High Availability

Financial services require extremely high uptime. In-house IT systems require periodic maintenance windows that can interrupt service. Leading cloud providers design for redundancy across multiple availability zones, enabling near-continuous availability. This is particularly important for payment processing and mobile banking applications, where even brief outages directly damage customer trust and revenue.

Flexibility and Elastic Scaling

Cloud’s pay-as-you-use billing model means financial institutions pay only for the compute and storage they consume. During high-demand periods — such as tax season, quarterly reporting, or major payment events — infrastructure can scale automatically. When demand normalises, resources scale back down, avoiding the waste associated with permanently provisioning for peak load. This elasticity is one of the most operationally significant advantages cloud offers over on-premises infrastructure.

Security and Compliance

Security in financial cloud deployments is nuanced. Private and hybrid cloud models allow banks to keep sensitive data within controlled environments while still benefiting from cloud agility for less sensitive workloads. Public cloud providers implement encryption at rest and in transit, along with permission-based access controls, audit logging, and compliance certifications (such as PCI-DSS, ISO 27001, and SOC 2). That said, banks must conduct their own due diligence on shared-responsibility models — the cloud provider secures the infrastructure, but the institution retains responsibility for application-level security and data governance.

Leading Cloud Applications in BFSI

Application Hosting

Banks host customer-facing web and mobile applications on cloud infrastructure to guarantee high availability and low latency. Cloud hosting eliminates single points of failure and enables content delivery at scale globally — important for institutions with large retail customer bases. Amazon Web Services (AWS), which launched in 2006 and is widely credited with commercialising the modern cloud model, remains one of the dominant platforms for this use case.

Payment Gateways and Funds Transfer

Cloud-based payment infrastructure is already deployed by major lenders for initiating and processing payments and fund transfers. Cloud enables unified customer experience across channels (mobile, web, branch), while the redundancy and uptime characteristics of cloud infrastructure reduce the risk of failed or delayed transactions — a critical concern in regulated payment environments.

ERP and CRM Systems

Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) platforms delivered as Software-as-a-Service (SaaS) represent one of the most widely adopted cloud use cases across industries. In financial services, SaaS-based CRM tools enable relationship managers to access complete customer profiles and interaction histories from any device, while SaaS ERP platforms streamline back-office financial operations. SaaS reduces the operational burden of software maintenance, patching, and version upgrades — freeing internal IT teams to focus on higher-value integration and customisation work.

Implementation Considerations: Security, Compliance, and Vendor Selection

Cloud adoption in the BFSI sector is not without complexity. Three areas require particular attention:

• Regulatory compliance: Financial institutions operate under sector-specific regulations that govern where data can reside, how it must be protected, and what audit trails must be maintained. Cloud deployment strategies must account for data residency requirements and jurisdiction-specific rules before workloads are migrated.
• Security posture: Adopting cloud does not automatically improve security. Misconfigured storage buckets, overly permissive IAM roles, and inadequate secrets management are common sources of cloud security incidents. A well-defined cloud security framework — covering identity, network, data, and logging — must be in place before sensitive financial workloads move to cloud.
• Vendor selection: Industry practitioners consistently recommend working only with established, well-documented cloud providers who offer financial services compliance certifications and dedicated support. Vendor lock-in risk should also be evaluated, particularly for core banking workloads.

Conclusion

Cloud computing is not merely a supporting technology for Industry 4.0 — it is the infrastructure layer that makes the Fourth Industrial Revolution possible at enterprise scale. For the BFSI sector specifically, cloud adoption delivers measurable advantages in cost structure, operational resilience, scalability, and the ability to deploy AI, analytics, and automation rapidly.

The shift to cloud in banking and finance is no longer a question of if, but of how and how fast. Institutions that approach cloud adoption with a clear strategy — covering security, regulatory compliance, workload selection, and vendor governance — will be best positioned to realise Industry 4.0’s full potential and maintain a competitive edge in an increasingly digital financial services landscape.

Where is DevOps headed this year? Let’s examine key trends around topics including training, metrics, and tools

In 2019, we saw numerous successful DevOps initiatives across large and small enterprises. If you were involved in one, congratulations! But what can we expect for the future of DevOps in 2020?

As thought leaders in the DevOps community, we believe the following ten trends will shape the next year of DevOps across the globe.

1. Agile and DevOps will increase collaboration between technology and business functions
Agile and DevOps are grassroots movements that started within technology. In many cases, however, Agile and DevOps have not been able to break out of technology. On the other hand, Agile has been adopted in other functions, including finance, human resources, procurement, and marketing. Some senior leaders are increasingly inviting their whole organizations to “become agile.”

However, this doesn’t seem to have helped the technology community join forces with their colleagues in these other functions. Because of the competitive pressure digital is putting on organizations, in 2020 we will start to see more collaboration across functions – with agile as the conversation starter.

Encourage your teams to talk to people from different functions about their experience using agile methods.
To speed this up, encourage your teams to talk to people from different functions about their experience using agile methods. Some helpful questions include: How are you doing agile? What are you doing? What is changing for you? What issues are you having? How could we work together to help to address some of these issues? These questions will help people from different functions get to know each other as people, and collaboration will improve.

[ Are you fighting skeptics? Read also: DevOps for doubters: How to deal with 9 kinds of people who push back. ]

2. Training and improving DevOps skills will become an organizational priority
DevOps requires trying out new technologies. Recent research from the DevOps Institute found that 55 percent of survey respondents prefer to hire into their DevOps teams from within their organization. Unfortunately, many companies don’t have the necessary skills to do this, and hiring new people might not be possible due to budget restraints.

One approach is to create internal training universities. This is what the courier delivery services firm FedEx did: The company knew it did not have adequate skills in its talent pool of engineers, and this led its CIO to initiate the FedEx Cloud Dojo, which teaches its own engineers modern software development and technologies and functions as a university for FedEx. The university has reskilled more than 2,500 software programmers.

Organizations that want to use DevOps to help advance their digital transformation must make drastic improvements in training, learning, and improving skills that are essential to DevOps. We expect to see a more proactive pursuit of this in 2020.

3. Upskilling and cross-skilling will lead to the rise of the T-shaped professional
Recognizing the strained talent market, organizations and individuals will invest heavily in upskilling and cross-skilling in order to meet accelerating demands for new skills. While all IT professionals will need to become more cross-domain competent, developers, in particular, will need to add new breadth to their skills portfolio in areas such as testing, containerization, infrastructure, AI, and security.

Developers in particular will need to add new breadth to their skills portfolio.
There will also be a stronger emphasis on core (soft) skills such as empathy, customer experience, and collaboration. Silos are starting to come down in many areas, and the need for everyone to become T-shaped, with depth and breadth of knowledge, will become necessary to enable and support innovation. All of this training and new collaboration (see Trend 1, above) will lead to more workers developing new technical and professional skills and personal qualities, adding new depth and capabilities to the individuals on your teams.

4. More teams will shift mindsets from “job done” to “value realized”
Value Stream Mapping can help change the way your teams think about the definition of done (DoD) from being “I did my job” to “the value is realized.” It is one of the most effective ways of changing behaviors and getting your teams to think about the end-to-end lifecycle of what they’re working on.

This is why the adoption of Value Stream Management is critical in 2020. It will enable you to automate the outputs from Value Stream Mapping for ongoing progress monitoring. This lets a team connect all parts of the ever-complex DevOps toolchains with system-derived data based on cycle time. Teams that adopt Value Stream Management in 2020 will be able to base their next improvement experiments on data-driven decisions and prioritizations.

[ Read also: DevOps terms: 10 essential concepts, explained. ]

5. Tool fatigue will worsen before it gets better
The number of tools and frameworks in technology is daunting. The challenges IT teams face to understand, interconnect, and apply these will continue, and in 2020, there is no real resolution in sight.

The competition in the DevOps toolchain is fierce and flourishing.
The competition in the DevOps toolchain is fierce and flourishing. Events and conferences are filled with technology and best-practice sessions. Books, blogs, and videos are flooding email inboxes, and thought leaders are eager to share their expertise. Additionally, more open source tools are emerging to integrate new technologies.

To survive the challenges of complexity, it’s becoming increasingly important to have an automation strategy. As you work to develop this, don’t lose sight of the actual problems you are trying to solve and how you can get there by leveraging your own teams.

6. DevOps will become more measurable and metrics will become better defined
“What gets measured gets managed.” This quote is still valid, more than 60 years after Peter Drucker referenced it in his book, “The Practice of Management.” What we all want to avoid, however, is measuring for metrics’ sake.

Avoid the syndrome of measuring for metrics’ sake.
For the next few years of DevOps, the continuous improvement metrics we know today will continue to be the key metrics that matter. In 2020, we expect more organizations will agree on what to measure and adopt these metrics. Those looking for support can lean on the performance metrics outlined in research from DevOps Research and Assessment (DORA), which cites five measures of Software Delivery and Operational Performance (SDO) that can be used as leading indicators of success for high-performing DevOps teams. Key benchmarks within the report provide guidance on areas teams must improve in 2020. Selecting these key metrics and populating them with data will provide insights into the value and journey of DevOps.

7. DevOps teams will earn more and experience increased job satisfaction
Employees who continue down the DevOps path in 2020 will see benefits that go directly into their wallet and their job satisfaction. Automation will allow employees to work on more value-adding work instead of mundane manual tasks, resulting in improved job satisfaction and reduced stress levels.

In particular, DevOps engineers who see their work in automating and collaborating for improved software delivery can expect their salary to be significantly higher than peers in traditional roles, such as system administrators. Investments in training and certifications will also positively impact the quality of code and therefore may improve the business results. This, in turn, might finally change the value equation within more organizations and give IT more strategic seats at the table. It will certainly improve how other functional areas work with IT now and in the future.

8. There just might be service management and DevOps reunification
With the recent release of ITILv4, 2020 will be an interesting year for organizations that have been adopting DevOps and service management frameworks. The development and management of software products needs agile techniques, with a focus on value co-creation in a way that reduces waste. DevOps, service management, and other best practices like SRE can coexist to align teams, meet stakeholder demands, and improve the value delivered. Because digital transformation is not achieved instantly across an organization, established companies should begin with best practices and methodologies that are suited to their needs by starting small – then learn, build expertise, and scale up.

[ Read also: DevOps vs. ITIL 4 vs. SRE: Stop the arguments. ]

9. A new generation of IT takes over
The number of people who remember the days before DevOps is starting to shrink. The younger generation of workers on today’s IT and DevOps teams don’t remember the strict silos, with clear lines around areas of responsibility such as infrastructure, operations, application design, development, testing, and security. They don’t remember how this caused a lot of transition work between teams and groups. They don’t know that product owners, business analysts, architects, developers, testers, release managers, system administrators, and infrastructure owners had to agree and coordinate on the planning, development, testing, deployment, operation, and management of a piece of software. Just typing that sentence was exhausting; imagine living it.

The number of people who remember the days before DevOps is starting to shrink.
When we celebrated the 10-year anniversary of DevOps in 2019, we saw the wall removed between Dev and Ops. This is a reason for all of us to celebrate, and in 2020, expect to see this happen as more and more organizations adopt DevOps.

10. Artificial Intelligence adoption will rise, and DevOps teams should check it out
AI and machine learning (ML) were recently rated as the most important enterprise technologies of the next decade, according to a recent report from ISACA. Both will play a critical role in next-generation IT operations and DevOps teams. AIOps will give DevOps teams the ability to analyze more data faster, allowing them to improve key processes, tasks, and decision-making. In 2020, expect to see more DevOps teams adopting these tools, which automate the ingestion of fast volumes of data, use ML to analyze the data, and have the ability to leverage knowledge for automation or decision-making.

The AIOps market has already gained momentum, with 22 percent of IT enterprises using ML and data sciences as part of their work. The vendor landscape is broad, with a variety of leaders. This year, the AIOps market will continue its shift from a science project to the pilot and experimental stage.

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Much work remains in 2020 to aid in the future of DevOps adoption. It will require more than focusing on tools and techniques. DevOps initiatives must be registered as change programs, requiring a time, resource, and priority commitment from all business leaders. We’re hopeful that some of these trends will be realized this year, and that DevOps will be recognized as a new way of working.