ML Model Deployment & MLOps

About

This skill provides comprehensive guidance for transitioning machine learning models from development to production-ready services. It facilitates the creation of high-performance FastAPI servers, optimized Docker containerization, and scalable Kubernetes orchestration. By implementing critical MLOps patterns—such as liveness/readiness probes, Pydantic input validation, and sophisticated drift detection—it helps developers prevent common pitfalls like silent model degradation, OOM kills, and deployment downtime.

Key Features

• Production-grade FastAPI server scaffolding with health/readiness probes
• 21 GitHub stars
• Containerization strategies for consistent environment parity
• Real-time model monitoring and statistical drift detection logic
• Automated CI/CD pipelines for model validation and performance testing
• Kubernetes orchestration patterns including canary and blue-green deployments

Use Cases

• Scaling ML workloads using Kubernetes with proper resource limits and health checks
• Wrapping trained models in high-performance REST APIs for web integration
• Implementing automated monitoring to detect model performance decay in production