9798288775192 - Dspy for LLM Engineers: Build Modular, Self-Optimizing AI Pipelines with Python for Large Language Model Development and Deployment di CLARKE, EVAN (11 risultati)

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New. Print on Demand.

Paperback. Condizione: New.

Paperback. Condizione: new. Paperback. Large Language Models (LLMs) like GPT-4, Claude, and Gemini are transforming how we build intelligent software. But shipping production-grade LLM systems requires more than just prompting - it demands modular design, observability, optimization, and robust integration. Dspy, a lightweight Python framework, enables engineers to build composable, introspectable, and self-evaluating pipelines for reasoning applications, retrieval-augmented generation (RAG), autonomous agents, and beyond.This book is written by a full-stack AI automation expert and practitioner, with deep insight into deploying real-world LLM applications. The content is grounded in proven software engineering principles, supported by hands-on examples, and inspired by the latest practices in prompt engineering, memory management, pipeline evaluation, and AI observability. Whether you're an ML engineer, software developer, or technical founder - this is the missing manual for building LLM-native software with Dspy.Dspy for LLM Engineers is your practical guide to mastering Dspy a declarative framework for building structured and testable AI workflows. This book takes you from environment setup to chaining modular components, managing memory and retrieval, using evaluators, deploying pipelines, and benchmarking for speed, cost, and accuracy. It's not just a guide it's a roadmap for designing scalable, explainable, and high-performance LLM applications with Python.Step-by-step tutorials for building modular pipelines with DspyReal-world examples of prompt templates, memory strategies, and RAG chainsTools and libraries for full-stack LLM developmentEvaluators and output scoring best practicesDeployment flow summaries for local and cloud environmentsAppendices with cheat sheets, templates, and glossariesBonus: Includes battle-tested prompt engineering templates, pipeline design patterns, and a glossary of 50+ LLM and AI terms.This book is for: Machine Learning Engineers integrating LLMs into productsSoftware Developers building AI-native appsData Scientists seeking modular, inspectable workflowsTechnical Founders & Hackers building AI tools and agentsBootcamp grads, students, and professionals shifting into AI developmentNo prior experience with Dspy is required. A solid understanding of Python and basic familiarity with LLM APIs (like OpenAI or Hugging Face) is recommended.In just a few focused sessions, you'll go from writing fragile prompts to designing self-improving chains, implementing vector memory, and deploying scalable RAG systems. Each chapter is crafted to deliver immediate value - no fluff, just hands-on guidance and reusable patterns. You'll accelerate your AI workflow expertise faster than you thought possible.If you're ready to go beyond prompt hacking and start building reliable, modular, and production-ready AI systems with Python, Dspy for LLM Engineers is your definitive guide.Grab your copy now - and become the LLM engineer every team wants. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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

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

Paperback. Condizione: new. Paperback. Large Language Models (LLMs) like GPT-4, Claude, and Gemini are transforming how we build intelligent software. But shipping production-grade LLM systems requires more than just prompting - it demands modular design, observability, optimization, and robust integration. Dspy, a lightweight Python framework, enables engineers to build composable, introspectable, and self-evaluating pipelines for reasoning applications, retrieval-augmented generation (RAG), autonomous agents, and beyond.This book is written by a full-stack AI automation expert and practitioner, with deep insight into deploying real-world LLM applications. The content is grounded in proven software engineering principles, supported by hands-on examples, and inspired by the latest practices in prompt engineering, memory management, pipeline evaluation, and AI observability. Whether you're an ML engineer, software developer, or technical founder - this is the missing manual for building LLM-native software with Dspy.Dspy for LLM Engineers is your practical guide to mastering Dspy a declarative framework for building structured and testable AI workflows. This book takes you from environment setup to chaining modular components, managing memory and retrieval, using evaluators, deploying pipelines, and benchmarking for speed, cost, and accuracy. It's not just a guide it's a roadmap for designing scalable, explainable, and high-performance LLM applications with Python.Step-by-step tutorials for building modular pipelines with DspyReal-world examples of prompt templates, memory strategies, and RAG chainsTools and libraries for full-stack LLM developmentEvaluators and output scoring best practicesDeployment flow summaries for local and cloud environmentsAppendices with cheat sheets, templates, and glossariesBonus: Includes battle-tested prompt engineering templates, pipeline design patterns, and a glossary of 50+ LLM and AI terms.This book is for: Machine Learning Engineers integrating LLMs into productsSoftware Developers building AI-native appsData Scientists seeking modular, inspectable workflowsTechnical Founders & Hackers building AI tools and agentsBootcamp grads, students, and professionals shifting into AI developmentNo prior experience with Dspy is required. A solid understanding of Python and basic familiarity with LLM APIs (like OpenAI or Hugging Face) is recommended.In just a few focused sessions, you'll go from writing fragile prompts to designing self-improving chains, implementing vector memory, and deploying scalable RAG systems. Each chapter is crafted to deliver immediate value - no fluff, just hands-on guidance and reusable patterns. You'll accelerate your AI workflow expertise faster than you thought possible.If you're ready to go beyond prompt hacking and start building reliable, modular, and production-ready AI systems with Python, Dspy for LLM Engineers is your definitive guide.Grab your copy now - and become the LLM engineer every team wants. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.