AI at Scale: Industrializing ML with Databricks

Explore how to industrialize machine learning applications at enterprise scale in this 29-minute video from Databricks. Learn about accelerating model delivery, optimizing data science workflows, and maintaining high standards of model governance. Discover the role of ML Engineering in industrializing ML pipelines and understand the management of end-to-end ML pipelines. See a demonstration of how the Databricks Lakehouse platform can be used to deliver ML pipelines at scale. Gain insights into ML Ops, data science at scale, feature stores, data science use cases, and comparisons with data warehouse dimensional models. Examine point-in-time accurate data, SOK for feature stores, and implementation on data lakes, including physical feature tables.

Intro
ML Ops: Data Science at Scale
Example: Feature Store
Data Science Use Cases
Comparison with Data Warehouse Le Dimensional Model Similarities
Point in Time Accurate Data
SOK for Feature Store
Implementation on the Data Lake
Physical Feature Tables