ML and Generative AI in the Data Lakehouse

Chapter 3. An Introduction to ML on Databricks

In this chapter, you’ll explore the essential components of ML workflows tailored for the Databricks platform. You’ll be guided through an end-to-end process, starting with exploratory data analysis (EDA) to build an understanding of your dataset, followed by feature engineering to transform raw data into a feature matrix for building robust ML models.

Next, you’ll focus on model development, including creation and training. After training, you’ll learn about model deployment and serving, setting up the necessary infrastructure for real-time inferencing to provide actionable insights at scale.

We will then cover MLflow, an open source platform, which streamlines the ML lifecycle from experimentation to deployment. Throughout this book, you’ll see how MLflow simplifies the ML workflow within Databricks, empowering you to efficiently build, track, and serve ML models.

By the end of this chapter, you’ll have a comprehensive understanding of the entire ML lifecycle in a Databricks Lakehouse environment, from data preparation to production-grade model serving.

The End-to-End ML Environment

In today’s data-driven landscape, the ability to efficiently build, deploy, and manage ML models is crucial for businesses aiming to stay competitive. Achieving this requires two things: robust infrastructure for data processing and model execution, and systematic tooling for managing the iterative, experimental nature of ML development.

Databricks ...