Productionizing Generative AI with Databricks

Turn your Generative AI experiments into scalable, production-ready systems.
Productionizing Generative AI with Databricks is the ultimate hands-on guide for machine learning engineers, data scientists, and architects who want to bring Large Language Models (LLMs) to life in the enterprise, using the power of the Databricks Lakehouse.

From training and fine-tuning to deployment and monitoring, this book shows you how to build, automate, and govern every stage of an LLM pipeline. You'll learn how to connect unstructured data with retrieval-augmented generation (RAG), manage experiments with MLflow, deploy models with Model Serving, and implement continuous delivery workflows with Databricks Asset Bundles.

Written in a clear, practical tone by an experienced practitioner, this guide makes complex MLOps patterns approachable and applicable. Each chapter offers actionable insights, real Databricks examples, and best practices drawn from real-world enterprise scenarios.

Inside you'll learn how to:

• Build and scale LLM pipelines using the Databricks Lakehouse.

• Apply fine-tuning and PEFT for custom generative models.

• Integrate Vector Search, RAG, and MLflow tracking seamlessly.

• Use Databricks Asset Bundles (DABs) for CI/CD automation.

• Implement monitoring, governance, and Responsible AI principles.

• Avoid common pitfalls when productionizing large-scale AI systems.

Whether you're building your first generative model or designing a full-scale enterprise AI platform, this book helps you move from experimentation to reliable, governed production systems.