Fundamentals Tutorial of Scala
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Efficient MLOps in a Kubernetes Environment
Kubernetes is powerful for managing production infrastructure, but it was never designed for AI/ML workloads. Learn how MLOps tooling bridges the gap between data science teams and Kubernetes clusters.
Opening the Door to Innovation with Data Superpowers
Empowering data teams with the right tools—AI, ML, and modern analytics platforms—removes bottlenecks, democratizes data access, and unlocks genuine business innovation. Here’s what that looks like in practice.
Google Cloud Integrates Developer Tools into Looker: What’s New
Google Cloud has released significant developer-focused upgrades to Looker, its business intelligence and analytics platform. Here’s a breakdown of what changed, including a redesigned IDE, new Looker Blocks for Google Marketing Platform, and a generally available extension framework.
Algorithmia Looks to Meld MLOps and DevOps
Algorithmia’s enterprise MLOps platform update brings local debugging, wider desktop tool integration, and expanded GPU support—bridging the gap between data science workflows and mainstream DevOps practices.
Google Cloud Enhances Looker with Performance Optimizations, Marketing Platform Support, and New Developer Tools
Google Cloud has released a significant set of Looker enhancements spanning modern BI features, foundational performance improvements, and updated data and development tooling—including full Google Marketing Platform support and a generally available Looker Marketplace.
Udacity and Microsoft Partner to Offer Machine Learning Scholarships for Azure Cloud
Udacity and Microsoft announced a two-phase scholarship programme giving thousands of learners access to machine learning training on Microsoft Azure, including hands-on labs in live Azure environments.
How AI and Machine Learning Are Transforming DevOps — and Why DevSecOps Is the Next Frontier
AI and machine learning are reshaping DevOps by automating analysis, accelerating delivery pipelines, and embedding security at every stage. Here is what engineering teams need to understand about the shift toward AI-powered DevSecOps.
Azure-hosted AI for finding code defects emitted – but does it work?
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The Pros and Cons of AI and ML in DevOps
AI and machine learning offer real benefits to DevOps teams—faster data analysis, smarter testing, and continuous experimentation—but deployment challenges, bias risks, and over-reliance on automation can undermine their value. Here is a balanced look at both sides.
3 test design principles to get you to continuous integration
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Bridging the Gap Between Admins and Developers in Salesforce DevOps
As Salesforce environments grow in complexity, organisations must bring admins and developers together under a shared DevOps practice. Here is how to overcome the cultural and technical barriers that stand in the way.
What’s Next in DevOps?
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Google Cloud AI Platform Pipelines: Simplifying Machine Learning Development with MLOps
Google’s Cloud AI Platform Pipelines (beta) brings MLOps to Google Cloud, letting teams deploy repeatable, auditable ML pipelines on GKE using Kubeflow Pipelines or TensorFlow Extended SDKs.
AppDynamics joins the dots in Cisco’s roadmap to multicloud
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Google Cloud AI Platform Pipelines: Beta Launch Explained
Google Cloud Platform announced the beta launch of Cloud AI Platform Pipelines, a managed ML workflow product built on TensorFlow Extended (TFX) and Kubeflow Pipelines (KFP). Here is what DevOps and MLOps engineers need to know.
Containers and Kubernetes: 3 Transformational Success Stories
How Expedia Group, Primerica, and others used containers and Kubernetes to modernise applications, accelerate delivery, and enable multi-cloud portability.
The 7-Step Plan for Successful AIOps Implementations
Deploying AIOps without a structured plan is one of the fastest ways to lose executive trust. This practical 7-step framework covers everything from initial planning and socializing change, through configuration, testing, and production deployment.
Google Cloud Machine Images: Simplifying Data Science VM Deployments
Google Cloud’s Machine Images feature lets system administrators and data scientists capture, clone, and restore full virtual machine environments — including multi-disk configurations and instance metadata — without manual reconfiguration.
MLOps Explained: What Happens When You Apply DevOps Principles to Machine Learning
MLOps is emerging as the discipline that brings DevOps-style governance, automation, and monitoring to machine learning systems. Here is what it means in practice and why it matters for engineering teams.