Python AI Programming, Second Edition (Paperback)
Language: English
Published by Gitforgits, 2026
- Softcover
- New
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
AbeBooks seller since October 12, 2005
Condition: New
£ 47.90
Quantity: 1 available
Add to basketItem description from seller
Paperback. The way we develop AI has completely changed. I mean, who builds models from scratch these days? Nobody.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 LearningsCall 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 ContentsWorking with ModelsPrompts that WorksReliable Outputs and ConversationsProgramming Prompts with DSPyEmbeddings and Semantic SearchRetrieval-Augmented GenerationGetting Data ReadyModel Needs CustomizationWorking with Images and SoundTools, Agents and MCPExamining AI with Evals and Safety TestsShipping with FastAPI and OpenTelemetry This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…
Seller Inventory # 9789349174511
- Title
- Python AI Programming, Second Edition (Paperback)
- Author
- Patrick J.
- Publisher
- Gitforgits
- Publication year
- 2026
- Condition
- new
- Binding
- Paperback
- Language
- English
- ISBN 10
- 9349174510
- ISBN 13
- 9789349174511
- Edition
- 2nd Edition
The way we develop AI has completely changed. I mean, who builds models from scratch these days? Nobody.
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
"Synopsis" may belong to another edition of this title.
Grand Eagle Retail
Bensenville, IL, U.S.A.
AbeBooks seller since October 12, 2005
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