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.

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