A feature store enables machine learning (ML) features to be registered, discovered, and used as part of ML pipelines, thus making it easier to transform and validate the training data that is fed into machine learning systems. Feature stores can also enable consistent engineering of features between training and inference, but to do so, they need a common data processing platform.
The Hopsworks Feature Store is a platform for managing and serving features for machine learning available as open-source, Enterprise or as a managed platform on AWS (www.hopsworks.ai). The Hopsworks Feature Store can also be integrated with Databricks.
Attend this webinar to:
- Learn how the Feature Store enables end-to-end ML pipelines to be factored into feature engineering and data science stages that each can run at different cadences.
- Learn how to write Python or Scala programs to compute features and register them in Hopsworks, browse and inspect features, and create train/test datasets from within a notebook
- Watch a demo of how to integrate the Hopsworks Feature Store with Databricks
Register now, we look forward to connecting with you soon.
Jim Dowling is CEO of Logical Clocks and an Associate Professor at KTH Royal Institute of Technology. He is lead architect of the open-source Hopsworks platform, a horizontally scalable data platform for machine learning that includes the industry’s first Feature Store.