MASTERING LLMOPS WITH DATABRICKS A Hands-On Guide to Building, Deploying, Monitoring, and Governing Large Language Model [#1042352]

MASTERING LLMOPS WITH DATABRICKS: A Hands-On Guide to Building, Deploying, Monitoring, and Governing Large Language Model Applications
English | October 14, 2026 | ASIN: B0HMZ331KR | 399 pages | Epub | 341.28 KB
Move Beyond AI Experiments. Learn to Build and Manage Production-Ready AI Applications.Large language models are changing how organizations work, but building an AI prototype is only the beginning. The real challenge is turning that prototype into an application that can be deployed, monitored, maintained, secured, and improved over time.Mastering LLMOps with Databricks provides a practical introduction to the tools, workflows, and principles used to manage large language model applications throughout their lifecycle.Whether you are developing your AI engineering skills or strengthening your understanding of production AI systems, this guide explores how Databricks and its supporting technologies can help bring data, models, and operational workflows together.Inside this book, you will explore how to:Understand LLMOps: Learn why large language models require more than conventional model development and how to approach their complete operational lifecycle.Work with Databricks: Understand the role of the Lakehouse platform, Delta Lake, and integrated data and AI workflows.Manage experiments with MLflow: Track experiments, organize model versions, record metrics, and improve reproducibility.Develop retrieval-augmented generation (RAG) systems: Explore how retrieval pipelines connect language models with relevant external knowledge.Deploy and monitor AI applications: Learn the principles behind model serving, performance monitoring, automated workflows, and ongoing maintenance.Strengthen AI governance: Understand access control, model lineage, responsible AI practices, security, and compliance considerations.Improve operational efficiency: Explore resource management, cost optimization, workflow automation, and practical approaches to maintaining production systems.Designed for practical learningThis book connects key LLMOps concepts with the tools and processes used in real-world AI engineering. It also explores workflow configurations, model management practices, deployment strategies, and supporting templates that can help readers understand how the pieces fit together.Who should read this book?This guide is intended for aspiring AI engineers, machine learning practitioners, data professionals, software developers, and technical students who want to understand how large language model applications are built and operated using Databricks and MLflow.You do not need to master every tool before beginning. A basic understanding of programming, data, or machine learning will help you get more from the material.If your goal is to move beyond experimenting with large language models and understand what it takes to operate AI applications responsibly and reliably, this book provides a structured starting point.