Practical MLflow for Generative AI on Databricks

Chapter 1. Introduction to MLflow for GenAI on Databricks

Artificial intelligence (AI) and machine learning (ML) development teams run into the same problems repeatedly: manually tracking training experiments, struggling to manage models in production, and working without a reliable monitoring stack. These gaps erode reliability, scalability, and reproducibility, and they get worse as projects grow. Managing the ML lifecycle needs a more structured approach.

MLflow was built to close those gaps. Since its first release in June 2018, the open source platform has worked to standardize and streamline ML development by offering solutions for tracking experiments, reproducing training runs, and managing models. Adoption has grown steadily: MLflow reached 10 million monthly downloads in November 2022 and passed 16 million by the end of 2023. Much of that growth comes from its open, flexible design. It integrates with widely used ML libraries and runs reliably across environments, from local machines to cloud platforms.

As enterprises build more generative AI (GenAI) applications with large language models (LLMs), effective lifecycle management becomes increasingly important. Compared to traditional machine learning, GenAI solutions are more complex and require disciplined practices to ensure they are implemented and managed sustainably over the long term.

This chapter introduces the fundamentals of using MLflow on the Databricks platform. It provides an overview of MLflow’s core components ...