Python AI Programming, Second Edition
Kickstart developing AI-ready apps with RAG, DSPy, MCP, agents, evals, observability and open-source models
Vectors, embeddings, retrieval, agents, and evaluation are all built from first principles inside the chapter that needs them. No mathematics. No machine learning background. No prior AI experience and no framework knowledge is required. We build with plain Python and small, single-purpose libraries.
About
About the Book
Today's developers are creating apps based on existing models, and this second edition teaches you how to do that in Python. We start with one API call and end up with a full production service that's all set to go. As you go through the chapters, you build one application that grows with you, so you never throw anything out. With retrieval, you can supply real documents, get reliable, structured output with Pydantic schemas, allow DSPy to optimise your prompts against a measured metric, and give your assistant eyes, ears and hands through vision, speech and tool calling.
The book is deliberately practical. There's no backpropagation or transformer that's been hand-built. Instead, there are working files, realistic cost estimates, actual failure modes, and the judgment to know when fine-tuning is worth it and when it's not. The rest of the book looks at stuff like the Model Context Protocol, evaluation sets that spot regressions before customers do, safety and privacy checks, and observability with FastAPI and OpenTelemetry.
Key Learnings
- Call hosted models reliably with retries, streaming, and controlled temperature settings.
- Enforce structured output using Pydantic schemas with automatic validation and correction.
- Build retrieval pipelines that ground every reply in your own documents.
- Prepare messy PDFs and web content into passages worth embedding.
- Optimise prompts programmatically with DSPy instead of hand-tuning them forever.
- Judge honestly when fine-tuning repays its cost and when it never will.
- Extract structured orders from photographs and transcribe spoken requests accurately.
- Give models tools safely, with step limits and confirmation gates.
- Expose capabilities through Model Context Protocol server’s reusable across applications.
- Measure quality with eval sets, then trace cost and latency in production.
Table of Contents
- How AI Works Today?
- Working with Models
- Prompts that Works
- Reliable Outputs and Conversations
- Programming Prompts with DSPy
- Embeddings and Semantic Search
- Retrieval-Augmented Generation
- Getting Data Ready
- Model Needs Customization
- Working with Images and Sound
- Tools, Agents and MCP
- Examining AI with Evals and Safety Tests
- Shipping with FastAPI and OpenTelemetry
Packages
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All packages include the ebook in the following formats: PDF and EPUB
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This book + Extras Downloadable (Neural Networks with Python, Second Edition)
Minimum price
Suggested price$56.99$49.99
- Neural Networks with Python, Second EditionExplore Transformers, ViTs, Diffusion, KANs, and SSMs using Python, NumPy and PyTorch. This book is written for data scientists and AI engineers who want depth without the dependency bloat, keeping its toolkit to five libraries and its focus on understanding. The book makes you capable to read any new architecture paper and recognise the parts, because you'll have built them yourself.
This book is also available in the following packages:
This book + Extras Downloadable (Private AI with Spark)
No Description Available
- Private AI with SparkDesign, package, and operate private AI locally using Apache Spark, batch pipelines, and vLLM acceleration Instead of relying on external APIs or cloud-hosted intelligence services, this book clearly demonstrates how Apache Spark can orchestrate data preparation, model training, batch inference, reporting, and LLM acceleration in a disciplined and transparent way.
- Minimum price
- $49.99
- Suggested price
- $56.99
- Private AI with Spark
This book + Extras Downloadable (Neural Networks with Python, 2nd Edition + Private AI with Spark)
No Description Available
- Neural Networks with Python, Second EditionExplore Transformers, ViTs, Diffusion, KANs, and SSMs using Python, NumPy and PyTorch. This book is written for data scientists and AI engineers who want depth without the dependency bloat, keeping its toolkit to five libraries and its focus on understanding. The book makes you capable to read any new architecture paper and recognise the parts, because you'll have built them yourself.
- Private AI with SparkDesign, package, and operate private AI locally using Apache Spark, batch pipelines, and vLLM acceleration Instead of relying on external APIs or cloud-hosted intelligence services, this book clearly demonstrates how Apache Spark can orchestrate data preparation, model training, batch inference, reporting, and LLM acceleration in a disciplined and transparent way.
- Minimum price
- $69.99
- Suggested price
- $76.99
- Neural Networks with Python, Second Edition
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Where others summarize, we construct step-by-step learning blueprints, cutting through clutter, banning the fluff, and ensuring every paragraph delivers hands-on value. Our audience isn’t learning from scratch—they’re leveling up with purpose, and we stand by them with code-first content, consistent project workflows, and a zero-redundancy approach.
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