Reseña del libro "MLflow Generative AI Engineering (en Inglés)"
Your Generative AI Application Works. But Is It Ready for Production? Building a RAG pipeline or AI agent is one thing. Making it reliable, observable, measurable, and maintainable in production is an entirely different engineering challenge. You may know how to connect Python applications to large language models, retrieve documents with vector databases, or build AI agents. But what happens when responses become inaccurate, retrieval quality deteriorates, prompts change, agents behave unpredictably, or failures become difficult to diagnose? MLflow Generative AI Engineering bridges the gap between experimental AI applications and production-oriented systems. Combining the practical approach of an MLflow 3 tutorial with the principles of LLMOps engineering, this book explores how to build, evaluate, trace, deploy, and manage Generative AI applications using Python, MLflow 3, LangChain, LangGraph, and Databricks. Through practical code examples and an evolving Production Knowledge Intelligence Platform, you'll discover how MLflow Generative AI workflows connect experiment tracking, retrieval-augmented generation (RAG), LLM evaluation, agent orchestration, and deployment into a coherent engineering system. Inside This Book, You'll Discover How To: Master MLflow 3 and experiment tracking: Configure tracking infrastructure, manage experiments, record metrics, and improve reproducibility. Build production RAG pipelines: Implement document ingestion, embeddings, semantic search, Chroma vector databases, and LangChain retrieval workflows. Improve RAG evaluation with Python: Measure retrieval quality, answer relevance, groundedness, and hallucination-related failures using custom scorers. Implement LLM tracing and observability: Investigate execution spans, model calls, retrieval failures, latency, and application errors with MLflow. Manage prompts and model lifecycles: Compare prompt variants, track model versions, optimize token usage, and support controlled releases. Develop and evaluate LangGraph agents: Build stateful workflows, integrate tool calling, and apply LangGraph agent evaluation to assess reliability and safety. Strengthen AI agent monitoring: Trace agent decisions, inspect tool execution, and diagnose unexpected behavior. Deploy with FastAPI and Docker: Explore containerization, automated testing, CI/CD pipelines, and deployment troubleshooting. Apply production LLMOps practices: Address monitoring, security, authorization, operational costs, reliability, and incident recovery. Explore Databricks MLflow: Understand managed tracking, Unity Catalog governance, model management, and enterprise AI deployment. More Than an MLflow Tutorial—A Practical Guide to Production Generative AI Engineering Unlike disconnected tutorials, this book follows a consistent application architecture, showing how experiment tracking, RAG evaluation, LLM observability, AI agent monitoring, and deployment fit together. Whether you're a Python developer, AI engineer, machine learning practitioner, or software professional transitioning into LLMOps, you'll gain practical engineering knowledge supported by code examples, architectural explanations, and troubleshooting techniques. Stop treating Generative AI as a collection of experiments. Start engineering systems built for real-world demands. Get your copy of MLflow Generative AI Engineering today and take your AI development skills beyond the prototype.
