Ready to get started with lakehouses, especially around DS/ML? This session is for you.



The journey from training models to taking them to production can be quite challenging. Teams face a large variety of issues: siloed data, inconsistent tools, complex infrastructure, and a lack of visibility into their model performance. Learn how the Databricks Lakehouse Platform provides a unified, data-centric ML environment that accelerates and simplifies machine learning lifecycle, with a standardized set of tools, frameworks and governance used across your lakehouse data.



In this session, you’ll learn how about lakehouses, and how to:

• Ingest, prepare and process data on a platform designed to handle production-scale ML training, including LLMs


• Leverage data science notebooks and MLflow to manually train and track your ML experiments or let AutoML do the experimentation for you


• Use real-time serving with fast autoscaling to save cost and maintain SLAs


• Monitor your deployed models for drift and accuracy


• Manage and govern all data and ML assets with Unity Catalog