Roberto Pizzlo (17 results)

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Paperback. Condition: new. Paperback. Agentic AI Systems: Build Multi-Agent Workflows with LangChain, MCP, RAG & Ollama(A Practical Guide to Local LLM Orchestration, Retrieval-Augmented Generation, and Autonomous Agents)Unlock the power of local LLMs, agentic AI architectures, and multi-agent orchestration with this hands-on guide designed for developers, AI engineers, and system architects building intelligent applications beyond the cloud. In an era where data privacy, autonomous workflows, and cost-effective deployments are critical, this book offers a production-ready blueprint using LangChain, Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), and Ollama.Whether you're designing AI copilots, deploying autonomous agents, or developing secure on-premise AI systems, this guide helps you go from concept to execution with confidence.What You'll Learn: Set up a complete agentic AI stack with LangChain, LangGraph, MCP, and OllamaRun private LLMs like Llama 3 and Mixtral with full control using OllamaFine-tune models with LoRA/QLoRA for domain-specific applicationsDesign and orchestrate multi-agent systems using LangGraph and graph-based coordinationBuild robust Retrieval-Augmented Generation pipelines using FAISS and ChromaImplement secure message-passing and streaming using MCPHandle authentication, observability, and compliance (GDPR, HIPAA, SOC 2)Deploy agents with Docker, Kubernetes, and scalable CI/CD pipelinesWho This Book Is For: AI engineers and backend developers working with LLMs and LangChainSecurity-conscious teams needing private and auditable AI workflowsDevOps and MLOps professionals deploying containerized AI systemsResearchers and tech leads building autonomous agent systemsAnyone interested in real-world agentic AI with local deployment capabilitiesUnlike cloud-reliant AI books or overly academic texts, Agentic AI Systems delivers actionable blueprints for building and deploying real systems on local infrastructure. You'll explore hands-on code, architecture diagrams, and reusable patterns that scale from laptops to clusters. No fluff-just proven strategies and reproducible workflows grounded in current LLM capabilities.Roberto Pizzlo is an AI infrastructure engineer and systems architect specializing in agentic orchestration and secure LLM deployments. Known for translating cutting-edge AI concepts into practical engineering, he brings a wealth of expertise in LangChain, LangGraph, RAG architectures, and edge AI systems. His experience bridges research, enterprise, and open-source ecosystems-making this book an essential guide for professionals navigating the fast-evolving world of autonomous AI.This guide reflects 2025 technologies and best practices, including the latest versions of LangChain, Ollama (v0.2.16+), CUDA 12.9, and RAG toolchains. It ensures your understanding remains relevant in a rapidly changing AI landscap 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 multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: new. Paperback. Unlock the full potential of Retrieval-Augmented Generation (RAG) with Building Retrieval-Augmented Agents with DSPy, the definitive guide to designing modular, declarative, and self-improving AI systems using Python and large language models (LLMs).Whether you're an AI developer, machine learning engineer, backend architect, or NLP researcher, this book delivers a hands-on, end-to-end approach to constructing intelligent agents using the powerful DSPy framework. You'll learn how to build robust RAG pipelines that seamlessly integrate vector search, document chunking, tool calling, memory handling, and modular LLM orchestration-while maintaining full control over optimization and traceability.Written by Roberto Pizzlo, a leading voice in the field of modern AI systems, this book is your launchpad into scalable, production-ready agentic workflows. With concise explanations, up-to-date DSPy examples, and best practices drawn from real-world deployments, this guide equips you to build agents that reason, retrieve, revise, and respond with precision.Inside You'll Learn: How to construct declarative RAG pipelines using DSPy's signature-driven programmingTechniques for chunking, embedding, indexing, and hybrid retrieval using FAISS, Qdrant, and WeaviateHow to integrate OpenAI, Claude, Cohere, or local LLMs via DSPy modulesOptimizing agents using MIPROv2, BootstrapFewShot, and assertion-based correction loopsTool-augmented workflows combining search, calculators, summarizers, and code interpretersReal-world case studies on building enterprise-grade knowledge botsDeployment strategies with FastAPI, Docker, and local embedding models via OllamaFuture directions in modular RAG, memory-augmented agents, and self-adaptive pipelinesWhy This Book?Unlike abstract theory-heavy texts, this guide focuses on practical implementation, real-world design patterns, and scalable agent infrastructure. It's built for the realities of 2024 and beyond, where precision, observability, and modular design are essential for AI production systems.If you're working with DSPy, Python, RAG, LLMs, retrievers, vector search, or frameworks like LangChain, LlamaIndex, or AutoGen, this book is a must-have resource in your AI development toolkit.About the Author: Roberto Pizzlo is a seasoned AI systems engineer and author with a growing portfolio of influential titles on intelligent agents, modular LLM architecture, and generative AI frameworks. Known for his clarity, technical depth, and practical insight, Pizzlo has helped thousands of professionals master cutting-edge AI technologies and deploy scalable NLP pipelines across industries.Perfect For: LLM engineers, AI developers, data scientists, backend teams, NLP practitioners, MLops professionals, and anyone building next-gen AI applications. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condition: new. Paperback. In a world where AI systems are scaling faster than ever, the ability to engineer context effectively has become the deciding factor between mediocre performance and breakthrough results. RAG and Context Engineering: Master Context Windows, Long-Term Memory, and Retrieval-Augmented Generation for Scalable AI is your definitive guide to building AI applications that think smarter, remember longer, and deliver more relevant answers - every time.This book takes you inside the cutting-edge techniques behind context windows, long-term memory integration, and retrieval-augmented generation (RAG). You'll discover how to structure, filter, and scale AI knowledge bases, seamlessly combine memory with vector search, and deploy production-ready systems capable of handling thousands - even millions - of documents.Written by Roberto Pizzlo, a seasoned AI systems engineer and technical author, this guide blends practical workflows with real-world examples to ensure you can execute every concept, not just understand it. Whether you are an AI developer, machine learning engineer, data scientist, or tech entrepreneur, you'll gain the expertise to: Architect scalable RAG pipelines with precisionIntegrate semantic search, ranking, and multi-source retrievalOptimize context usage for cost, speed, and accuracyMaintain and update AI knowledge bases with confidenceFuture-proof your workflows for larger context windows and emerging AI architecturesWith its clear, actionable, and execution-ready approach, this book delivers the depth you need without unnecessary fluff. You'll finish with the ability to not only design smarter AI but also ensure it adapts to the fast-evolving landscape of autonomous, context-driven systems.If you want to master context engineering, vector search, and RAG at scale, this is the playbook trusted by forward-thinking AI professionals who need results now - and are ready to lead the next wave of intelligent applications. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, 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.

Paperback. Condition: new. Paperback. Agentic AI Systems: Build Multi-Agent Workflows with LangChain, MCP, RAG & Ollama(A Practical Guide to Local LLM Orchestration, Retrieval-Augmented Generation, and Autonomous Agents)Unlock the power of local LLMs, agentic AI architectures, and multi-agent orchestration with this hands-on guide designed for developers, AI engineers, and system architects building intelligent applications beyond the cloud. In an era where data privacy, autonomous workflows, and cost-effective deployments are critical, this book offers a production-ready blueprint using LangChain, Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), and Ollama.Whether you're designing AI copilots, deploying autonomous agents, or developing secure on-premise AI systems, this guide helps you go from concept to execution with confidence.What You'll Learn: Set up a complete agentic AI stack with LangChain, LangGraph, MCP, and OllamaRun private LLMs like Llama 3 and Mixtral with full control using OllamaFine-tune models with LoRA/QLoRA for domain-specific applicationsDesign and orchestrate multi-agent systems using LangGraph and graph-based coordinationBuild robust Retrieval-Augmented Generation pipelines using FAISS and ChromaImplement secure message-passing and streaming using MCPHandle authentication, observability, and compliance (GDPR, HIPAA, SOC 2)Deploy agents with Docker, Kubernetes, and scalable CI/CD pipelinesWho This Book Is For: AI engineers and backend developers working with LLMs and LangChainSecurity-conscious teams needing private and auditable AI workflowsDevOps and MLOps professionals deploying containerized AI systemsResearchers and tech leads building autonomous agent systemsAnyone interested in real-world agentic AI with local deployment capabilitiesUnlike cloud-reliant AI books or overly academic texts, Agentic AI Systems delivers actionable blueprints for building and deploying real systems on local infrastructure. You'll explore hands-on code, architecture diagrams, and reusable patterns that scale from laptops to clusters. No fluff-just proven strategies and reproducible workflows grounded in current LLM capabilities.Roberto Pizzlo is an AI infrastructure engineer and systems architect specializing in agentic orchestration and secure LLM deployments. Known for translating cutting-edge AI concepts into practical engineering, he brings a wealth of expertise in LangChain, LangGraph, RAG architectures, and edge AI systems. His experience bridges research, enterprise, and open-source ecosystems-making this book an essential guide for professionals navigating the fast-evolving world of autonomous AI.This guide reflects 2025 technologies and best practices, including the latest versions of LangChain, Ollama (v0.2.16+), CUDA 12.9, and RAG toolchains. It ensures your understanding remains relevant in a rapidly changing AI landscap This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.