Recommended Reading

Books that shaped how I think about ML engineering.

Machine Learning Engineering — Andriy Burkov Bridges the gap between academic ML and production systems. Covers the full lifecycle: feature engineering, training, deployment, monitoring. The reason I treat config management (Hydra) and experiment tracking (MLflow) as non-negotiable.

AI Engineering — Chip Huyen Practical guide to building applications with foundation models. Covers prompt engineering, RAG, fine-tuning, evaluation, and deployment. Essential for anyone working with LLMs in production.

Both books share the same core thesis: successful ML requires system-level thinking, not just accurate models. That principle drives everything I build — from config-driven experiments to always shipping with a demo and monitoring.