Python programming second edition von patrick (9 Ergebnisse)

Zustand: New.

Paperback. Zustand: new. 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.…

PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 202 pp. Englisch.…

Paperback. Zustand: new. 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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Zustand: new. 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 our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…

Taschenbuch. Zustand: Neu. Python AI Programming, Second Edition | Kickstart developing AI-ready apps with RAG, DSPy, MCP, agents, evals, observability and open-source models | Patrick J | Taschenbuch | Englisch | 2026 | GitforGits | EAN 9789349174511 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …