Python for AI Engineering: The Practical Guide to Using Python for Machine Learning, Data Pipelines, and AI Application Development

This book is written for people who already know the basics of Python and want to understand how all the pieces fit together in real AI projects. You will learn how to build data pipelines that do not collapse, how to serve models in production, how to work with large language models through code, and how to architect maintainable AI applications. Every chapter focuses on practical outcomes, actionable workflows, and the kind of hard-won knowledge that usually takes years of trial and error to accumulate.

Inside, you will discover:

• How to set up a Python environment that survives production

• Data wrangling at scale with Pandas and Polars

• Building data pipelines that handle real-world complexity

• Machine learning workflows that go beyond the notebook

• Deep learning with PyTorch for production systems

• Working with LLMs and building retrieval-augmented generation systems

• Serving models, tracking experiments, and monitoring AI systems in production

The AI engineering field is young, chaotic, and full of contradictory advice. This book does not claim to have all the answers, but it provides the principles that guide professional engineering in any context: write code that is testable, build pipelines that fail gracefully, and always keep the end user in mind when making technical decisions.