ML and Generative AI in the Data Lakehouse

Chapter 6. ML at Scale

ML has rapidly evolved from an experimental research field into a critical pillar for modern data-driven organizations. As data sources multiply, from transactional databases to IoT devices and social media streams, businesses find themselves grappling with exponentially growing datasets. Traditional single-node ML tools such as scikit-learn can handle moderate volumes of data effectively but often become bottlenecks when confronted with the sheer scale and complexity found in enterprise environments. Even when deployed on the Databricks platform, these toolkits typically leverage only a single node for processing, failing to take full advantage of Databricks’ distributed compute capabilities.

This chapter explores how the Databricks Lakehouse platform, Apache Spark, and Delta Lake together form a robust, scalable solution for enterprise ML. By combining distributed computing with ACID transactions, schema enforcement, and a unified interface for data engineering and analytics, these technologies allow practitioners to seamlessly move from raw data ingestion to advanced ML model training. We will discuss how Spark handles distributed data processing, the role of Delta Lake in managing large datasets reliably, and how Lakeflow Jobs and Lakeflow Spark Declarative Pipelines in the Databricks ecosystem orchestrate complex ML pipelines in both batch and real-time scenarios.

To bring these concepts to life, this chapter concludes with a hands-on example using ...