The first time I saw a cloud bill exceed a million dollars in a single month, I knew something had fundamentally changed about how we needed to think about infrastructure. This wasn’t a massive enterprise with unlimited budgets—it was a mid-sized company that had enthusiastically embraced “cloud-first” without understanding what that commitment actually meant financially. […]
Read more →Month: November 2025
Airflow on Kubernetes in Production: Architecture, Deployment, and Cost Optimization
Production-tested patterns for running Apache Airflow on Kubernetes with the KubernetesExecutor. Covers architecture, deployment, auto-scaling, cost optimization, and real-world case studies achieving 40-60% cost savings.
Read more →Microsoft Azure AI Foundry: The Complete Guide to Enterprise AI Development
Introduction: Microsoft Azure AI Foundry (formerly Azure AI Studio) represents Microsoft’s unified platform for building, evaluating, and deploying generative AI applications. Announced at Microsoft Ignite 2024, AI Foundry consolidates Azure’s AI capabilities into a single, cohesive experience that spans model selection, prompt engineering, evaluation, fine-tuning, and production deployment. With access to Azure OpenAI models, Meta […]
Read more →Visual Studio 2026 Transforms Developer Productivity with AI-Powered Intelligence and Cloud-Native Tooling
Introduction: After more than two decades working with Microsoft’s flagship IDE, I’ve witnessed Visual Studio evolve from a Windows-centric development tool into a comprehensive, AI-powered development platform. Visual Studio 2026, released alongside .NET 10, represents the most significant leap forward in the IDE’s history. This isn’t merely an incremental update—it’s a fundamental reimagining of how […]
Read more →Tips and Tricks – Use Multi-Stage Docker Builds for Smaller Images
Reduce container image size by separating build and runtime stages.
Read more →MLOps Excellence with MLflow: From Experiment Tracking to Production Model Deployment
MLflow has emerged as the leading open-source platform for managing the complete machine learning lifecycle, from experimentation through deployment. This comprehensive guide explores production MLOps patterns using MLflow, covering experiment tracking, model registry, automated deployment pipelines, and monitoring strategies. After implementing MLflow across multiple enterprise ML platforms, I’ve found that success depends on establishing consistent […]
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