Practical MLflow for Generative AI on Databricks

Practical MLflow for Generative AI on Databricks

Build High-Quality AI Agents from Prompt Design to Production

Practical MLflow for Generative AI on Databricks is a hands-on guide to building, managing, evaluating, deploying, and monitoring Generative AI applications, AI agents, and Large Language Model (LLM) solutions using MLflow and Databricks.

Generative AI is rapidly moving from experimentation and research into real-world enterprise applications. However, building a successful GenAI application requires more than designing a good prompt or selecting an LLM. Organizations need reliable tools and processes for prompt management, application tracking, evaluation, optimization, deployment, monitoring, and lifecycle management.

This practical book helps bridge the gap between cutting-edge Generative AI research and production-ready enterprise AI applications. It explains how MLflow, the popular open source platform for machine learning and AI lifecycle management, can be used with Databricks to develop and operationalize modern GenAI applications.

Written by Nuwan Ganganath, Julie Nguyen, and Chang Shi Lim, AI and machine learning specialists from Databricks, this book takes you through the complete development journey of an agentic chatbot and AI agent, from initial prompt design through production deployment.

Through practical examples, interactive exercises, technical explanations, and real-world code, you'll learn how to build robust LLM applications and AI agents that can be evaluated, optimized, deployed, and monitored in modern enterprise environments.

Learn MLflow for Generative AI

You'll begin by understanding the fundamentals of MLflow and its core components, followed by practical guidance on applying MLflow to Generative AI and LLM applications on Databricks.

Learn how MLflow can help you manage different aspects of the GenAI application lifecycle, including prompts, models, agentic chains, evaluation, deployment, and monitoring.

Whether you're working with large language models, AI agents, or enterprise GenAI applications, this book provides practical techniques for bringing AI projects from experimentation to production.

Build and Manage AI Agents

Discover how to build an AI agent and agentic chatbot step by step. Learn how prompts, LLMs, tools, workflows, and agentic chains can work together to create sophisticated Generative AI applications.

You'll learn how to:

• Design and manage effective prompts
• Build GenAI applications using MLflow
• Track AI applications and agentic workflows
• Manage LLM applications throughout their lifecycle
• Build AI agents and agentic chatbots
• Develop and manage agentic chains
• Apply practical LLMOps techniques
• Prepare GenAI applications for production

Evaluate and Optimize Generative AI Applications

Developing an AI application is only the beginning. Reliable Generative AI evaluation is essential for understanding application quality and improving performance.

This book explores practical approaches for evaluating GenAI applications using a combination of automated LLM-assisted judges and human-in-the-loop review.

Learn how to assess AI-generated responses, identify weaknesses, optimize application performance, and establish effective evaluation processes for enterprise AI applications.

Topics include:

• LLM evaluation
• Generative AI evaluation
• Automated AI evaluation
• LLM-assisted judges
• Human-in-the-loop evaluation
• GenAI application optimization
• AI quality assessment
• AI application performance
• Evaluation of AI agents and LLM applications

Deploy Generative AI Applications to Production

Moving an AI prototype into a reliable production environment presents a new set of challenges. This book focuses on the practical requirements of deploying production-ready Generative AI applications and AI agents.

Learn how to use the scalable infrastructure and monitoring capabilities of Databricks to deploy and manage enterprise GenAI applications.

Explore practical considerations for:

• AI agent deployment
• Generative AI deployment
• LLM application deployment
• Production-ready AI applications
• AI application monitoring
• GenAI observability
• Enterprise AI infrastructure
• Scalable Generative AI applications
• AI lifecycle management

MLflow, Databricks, and LLMOps

As organizations adopt Generative AI, traditional machine learning workflows are evolving into LLMOps and GenAI application lifecycle management.

Practical MLflow for Generative AI on Databricks demonstrates how MLflow and Databricks can support the development and operationalization of modern AI applications.

You'll gain practical knowledge of:

• MLflow
• Databricks
• Generative AI
• Large Language Models (LLMs)
• AI Agents
• Agentic AI
• LLMOps
• MLOps
• Prompt Engineering
• Prompt Management
• AI Evaluation
• AI Deployment
• AI Monitoring
• Enterprise AI
• GenAI Application Development

What You'll Learn

After reading this book, you'll be able to:

• Understand MLflow fundamentals and core components
• Apply MLflow to Generative AI applications on Databricks
• Build, track, and manage GenAI applications
• Manage prompts and agentic workflows
• Build AI agents and agentic chatbots
• Develop practical LLM applications
• Evaluate GenAI applications using automated and human review
• Optimize the performance and quality of AI applications
• Deploy production-ready Generative AI applications
• Monitor AI applications using Databricks capabilities
• Apply LLMOps and MLOps principles to enterprise AI projects
• Move from GenAI experimentation and prototypes to scalable production systems

Who Is This Book For?

This book is ideal for AI and machine learning professionals, data scientists, ML engineers, software developers, AI engineers, LLM developers, MLOps professionals, LLMOps practitioners, data engineers, and technology professionals who want to build and operationalize Generative AI applications.

It is particularly useful for professionals working with Databricks, MLflow, Large Language Models, AI agents, agentic AI, enterprise AI, and production GenAI applications.

Whether you're beginning to explore Generative AI on Databricks or already developing enterprise LLM applications, this book provides practical knowledge and real-world techniques for taking AI solutions from prompt design to production.

Key Topics Covered

• MLflow for Generative AI
• MLflow on Databricks
• Generative AI
• GenAI application development
• AI agent development
• Agentic AI
• Agentic chatbots
• Large Language Models
• LLM applications
• Prompt engineering
• Prompt design
• Prompt management
• LLMOps
• MLOps
• AI application tracking
• Generative AI evaluation
• LLM evaluation
• LLM-assisted evaluation
• Human-in-the-loop AI
• AI application optimization
• AI agent deployment
• GenAI deployment
• AI monitoring
• AI observability
• Enterprise AI
• Production-ready AI applications
• Databricks AI infrastructure
• AI lifecycle management

Why Choose Practical MLflow for Generative AI on Databricks?

Generative AI development is rapidly evolving, and organizations need practical approaches for managing AI applications beyond the prototype stage. This book focuses on the operationalization of Generative AI, giving readers practical guidance for developing, evaluating, deploying, and monitoring real-world AI applications.

By combining MLflow, Databricks, Generative AI, AI agents, LLM evaluation, and LLMOps, the book provides a practical roadmap for professionals who want to build reliable and scalable enterprise AI solutions.

Practical MLflow for Generative AI on Databricks is a valuable resource for anyone looking to understand how to take Generative AI and AI agent applications from prompt design to production.

Related Search Terms

MLflow book, MLflow Databricks book, MLflow for Generative AI, Practical MLflow, Generative AI Databricks, Databricks Generative AI, Generative AI book, GenAI book, AI agents book, AI agent development, Agentic AI, agentic chatbot development, LLMOps, LLMOps book, MLOps, Large Language Models, LLM applications, LLM application development, prompt engineering, prompt design, prompt management, MLflow AI agents, MLflow LLM, MLflow evaluation, MLflow monitoring, AI application development, Generative AI application development, AI deployment, Generative AI deployment, AI evaluation, LLM evaluation, AI monitoring, GenAI monitoring, enterprise AI, enterprise Generative AI, production AI, production-ready AI applications, Databricks AI, Databricks machine learning, Databricks LLM, Databricks AI agents, scalable AI applications, AI lifecycle management, human-in-the-loop AI, automated LLM evaluation, AI agent deployment, agentic applications.