Books

This is the clearest map I've found for turning LLM demos into production systems. Chip covers the full stack: data, evaluation, deployment, cost, and the product decisions that actually matter when users depend on your model.

What stuck with me most: treating AI engineering as a discipline separate from training foundation models. Retrieval, agents, guardrails, and observability aren't afterthoughts: they're the job.

I keep coming back to the chapters on RAG evaluation and agent workflows when scoping client projects. If you ship LLM features to real users, this belongs on your desk.

Find it on Link, Google Books, Amazon, or O'Reilly.

Before agents and chat UIs, this book taught me how to think about ML as infrastructure: data pipelines, training loops, deployment, monitoring, and the feedback cycles that keep models useful over time.

The framing of ML systems as living products, not one-off notebooks, changed how I approach MLOps and client deliverables. Feature stores, batch vs. online inference, and drift detection are explained with the clarity of someone who's operated at scale.

Even in the LLM era, the systems thinking here is foundational. Production AI still needs reliable data, evaluation, and iteration: this book is where I learned that mindset.

Find it on Link, Google Books, Amazon, or O'Reilly.