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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Feature Engineering at Scale: Building Production Feature Stores and Real-Time Serving Pipelines

Introduction: Feature engineering remains the most impactful activity in machine learning, often determining model success more than algorithm selection. This comprehensive guide explores production feature engineering patterns, from feature stores and versioning to automated feature generation and real-time feature serving. After building feature platforms across multiple organizations, I’ve learned that success depends on treating features […]

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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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