Production Model Deployment Patterns: From REST APIs to Kubernetes Orchestration in Python

After deploying hundreds of ML models to production across startups and enterprises, I’ve learned that model deployment is where most AI projects fail. Not because the models don’t work—but because teams underestimate the engineering complexity of serving predictions reliably at scale. This article shares production-tested deployment patterns from REST APIs to Kubernetes orchestration. 1. The […]

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Migration Guide: From Semantic Kernel & AutoGen to Microsoft Agent Framework – Part 10

Complete migration guide from Semantic Kernel and AutoGen to Microsoft Agent Framework. Before/after code examples and step-by-step instructions.

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MCP Integration & External Tool Connectivity in Microsoft Agent Framework – Part 9

Connect AI agents to external tools via Model Context Protocol. Learn MCP servers, Microsoft 365 integration, and building custom MCP servers.

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BigQuery Unleashed: Building Enterprise Data Warehouses That Scale to Petabytes

Introduction: BigQuery stands as Google Cloud’s crown jewel—a serverless, petabyte-scale data warehouse that has fundamentally changed how enterprises approach analytics. This comprehensive guide explores BigQuery’s enterprise capabilities, from columnar storage and slot-based execution to advanced features like BigQuery ML, BI Engine, and real-time streaming. After architecting data platforms across all major cloud providers, I’ve found […]

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Production-Ready Agents: Observability, Security & Deployment – Part 8

Deploy AI agents to production with enterprise-grade observability, security, and resilience. Complete guide to OpenTelemetry, content safety, and Azure deployment.

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The Cloud Bill Always Comes Due: Hard Lessons in FinOps from a Decade of Enterprise Cloud Migrations

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. […]

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