Deep-dive articles on machine learning, AI engineering, and production ML systems
How to build a complete MLOps pipeline — data versioning with DVC, experiment tracking with MLflow, model registry, automated retraining, and deployment gates.
Data drift vs concept drift — detection methods, monitoring dashboards with Evidently AI, and automated alerting strategies for production ML systems.
From model pickle to production FastAPI — async inference, input validation with Pydantic, rate limiting, health checks, and Docker deployment.
Best practices for containerizing ML code — multi-stage builds, GPU support, model caching, and the Dockerfile patterns that cut image sizes by 70%.
INT8 quantization, structured pruning, and distillation — how to shrink model size by 90% while keeping 95% of accuracy for edge deployment.
I build custom ML models, AI agents, computer vision, and automation — from idea to production.