Isbn: 9798298720472 (4 results)

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condition: new. Paperback. Master the Future of AI Systems with DSPy and Context EngineeringIn today's fast-moving world of large language models, context engineering is the key to building reliable, scalable, and intelligent applications. This hands-on guide shows you how to unlock the full potential of DSPy, the framework designed to simplify, optimize, and automate AI pipelines.Whether you're a developer, data scientist, or AI enthusiast, this book takes you step by step through building self-improving pipelines, tuning prompts and parameters automatically, integrating with Langfuse and MLflow, and deploying at scale in cloud environments. Every concept is paired with practical, executable examples-so you don't just learn the theory, you apply it immediately to real-world scenarios like retrieval-augmented generation (RAG).What sets this book apart is its clear, professional style and its focus on production-ready systems. By the end, you'll know how to monitor, improve, and maintain DSPy applications with confidence-turning prototypes into robust, enterprise-grade solutions.Why this book?Comprehensive yet practical: Covers everything from DSPy basics to advanced optimization loops.Future-proof skills: Learn how to scale across multiple models, providers, and production environments.Author credibility: Written by Roberto Pizzlo, a technology author known for distilling complex systems into clear, actionable guides.This isn't just another AI book-it's your hands-on companion for mastering the art of context engineering and building intelligent systems that get better with time.Inside the Book (Table of Contents Highlights)Chapter 1: Foundations of Context Engineering with DSPyChapter 4: Building Retrieval-Augmented Generation PipelinesChapter 6: Ensuring Reliability with Assertions and EvaluationChapter 8: Experiment Tracking with Langfuse and MLflowChapter 9: Building Self-Improving Pipelines with DSPy CompilersChapter 10: Deploying and Maintaining DSPy Applications in ProductionAppendices: API Quick Reference, Troubleshooting, Tools, and Glossary This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condition: new. Paperback. Master the Future of AI Systems with DSPy and Context EngineeringIn today's fast-moving world of large language models, context engineering is the key to building reliable, scalable, and intelligent applications. This hands-on guide shows you how to unlock the full potential of DSPy, the framework designed to simplify, optimize, and automate AI pipelines.Whether you're a developer, data scientist, or AI enthusiast, this book takes you step by step through building self-improving pipelines, tuning prompts and parameters automatically, integrating with Langfuse and MLflow, and deploying at scale in cloud environments. Every concept is paired with practical, executable examples-so you don't just learn the theory, you apply it immediately to real-world scenarios like retrieval-augmented generation (RAG).What sets this book apart is its clear, professional style and its focus on production-ready systems. By the end, you'll know how to monitor, improve, and maintain DSPy applications with confidence-turning prototypes into robust, enterprise-grade solutions.Why this book?Comprehensive yet practical: Covers everything from DSPy basics to advanced optimization loops.Future-proof skills: Learn how to scale across multiple models, providers, and production environments.Author credibility: Written by Roberto Pizzlo, a technology author known for distilling complex systems into clear, actionable guides.This isn't just another AI book-it's your hands-on companion for mastering the art of context engineering and building intelligent systems that get better with time.Inside the Book (Table of Contents Highlights)Chapter 1: Foundations of Context Engineering with DSPyChapter 4: Building Retrieval-Augmented Generation PipelinesChapter 6: Ensuring Reliability with Assertions and EvaluationChapter 8: Experiment Tracking with Langfuse and MLflowChapter 9: Building Self-Improving Pipelines with DSPy CompilersChapter 10: Deploying and Maintaining DSPy Applications in ProductionAppendices: API Quick Reference, Troubleshooting, Tools, and Glossary This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…