The Hopsworks Feature Store is a platform for managing and serving features for machine learning that also integrates Kubeflow and On-Premises Clusters (Hadoop)
- Available as open-source, Enterprise, or as a managed platform on AWS (www.hopsworks.ai)
- API support for ingesting features as dataframes (Spark/Pandas) with data validation and feature statistics (Deequ)
- Catalog and tag features and search for them in Hopsworks
- Feature computation either in Hopsworks or external (Databricks, AWS EMR, etc)
- Offline feature store with scale-out storage of feature data (Apache Hive + HopsFS - with data stored in S3 on AWS) and easy generation of train/test data on S3/HDFS/HopsFS
The Enterprise Feature Store also includes integration with Kubeflow and On-Premises Clusters (Hadoop), users can write Python or Scala programs to compute features and register them in Hopsworks, browse and inspect features, and create train/test datasets.
During this webinar we will introduce the concept of a Feature Store and how it helps manage data for AI. We will walk-through the Hopsworks Feature Store, introducing its concepts and how you can use it from Kubeflow and On-Premises Clusters (Hadoop) for feature engineering, as a feature registry, for creating train/test datasets for ML, and as an online Feature Store to build feature vectors for online applications with low latency.