Enable Production ML with Databricks Feature Store

Productionalizing ML models is hard. In fact, very few ML projects make it to production, and one of the hardest problems is data! Most AI platforms are disconnected from the data platform, making it challenging to keep features constantly updated and available in real-time. Offline/online skew prevents models from being used in real-time or, worse, introduces bugs and biases in production. Building systems to enable real-time inference requires valuable production engineering resources. As a result of these challenges, most ML models do not see the light of day.
Learn how you can simplify production ML using Databricks Feature Store, the first feature store built on the data lakehouse. Data sources for features are drawn from a central data lakehouse, and the feature tables themselves are tables in the lakehouse, accessible in Spark and SQL for both machine learning and analytics use cases. Features, data pipelines, source data, and models can all be co-governed in a central platform. Feature Store is seamlessly integrated with Apache Spark™, enabling automatic lineage tracking, and with MLflow, enabling models to look up feature values at inference time automatically. See these capabilities in action and how you can use it for your ML projects.
Connect with us:
Website: databricks.com
Facebook: / databricksinc
Twitter: / databricks
LinkedIn: / data. .
Instagram: / databricksinc

[@datadata96]

Domino Data Lab also has a feature store now!

[@raghugoud-te4jj]

Thanks for your info. Is the label required for the feature store table? because we don't have labels for new data. Shall we create a dummy label column otherwise?

[@gaborjenei5439]

Is the notebook presented available somewhere?

[@risebyliftingothers]

Good content but seemed bit rushed through and unprepared presenters

[@007mrran]

Girl presentation is poor .. look like she is learning by doing so.. waste of time