Building AI Systems with Python

Building AI Systems with Python is a hands-on guide for readers who want to move from isolated experiments to AI software that can actually be shipped. Martin Hander keeps the focus on practical engineering: building solid data pipelines, choosing sensible baselines, testing what matters, and then extending that foundation into modern deep learning and generative AI workflows.

From data foundations to production-minded AI

The opening sections establish the everyday building blocks of applied machine learning: NumPy, Pandas, PyArrow, scikit-learn, data loading, evaluation, testing, and reproducible project structure. That grounding matters here, because the book is clearly written for builders who need systems that stay understandable after the first prototype is complete.

Modern PyTorch workflows, explained clearly

From there, the book moves through PyTorch-first topics including optimization, transformers, diffusion, multimodal learning, classical models that still matter, and reinforcement learning in practice. The chapter progression suggests a strong emphasis on how these techniques fit into a working engineering stack, not just how they look in theory.

LLMs, agents, and practical GenAI patterns

The later sections focus on the tools and workflows that define current AI development: model adaptation with LoRA and QLoRA, quantization, prompt engineering, retrieval-augmented generation, tool calling, evaluation, observability, and safety-minded execution. That makes the book especially useful for readers building LLM applications, agentic workflows, or internal AI systems where reliability and control matter as much as capability.

Who this book suits best

• Machine learning and AI engineers
• Software developers moving into applied AI
• Data scientists building production systems
• MLOps and LLMOps practitioners
• Technical readers who prefer working code and systems thinking over demos alone

If you want an ebook that treats AI as an engineering discipline rather than a collection of flashy examples, this title fits that brief well. It reads like a book for people who expect their models to live in the real world. 💻