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Hopsworks supports free-text search for ML assets:

  • features,
  • feature groups,
  • feature views,
  • training data,
  • models, and
  • deployments.

You can use the search bar at the top of your project to free-text search for the names or descriptions of any ML asset. You can also search using keywords or tags that are attached to an ML asset. A keyword is a single user-defined word attached to an ML asset. Keywords can be used to help it make it easier to find ML assets or understand the context in which they should be used (for example, PII could be used to indicate that the ML asset is based on personally identifiable information.

However, it may be preferable to have a stronger governance framework for ML assets than keywords alone. For this, you can define a schematized tag, defining a list of key/value tags along with a type for a value. In the figure below, you can see an example of a schematized tag with two key/value pairs: pii of type boolean (indicating if this feature group contains PII data), and owner of type string (indicating who the owner of the data in this feature group is). Note there is also a keyword defined for this feature group called eu_region, indicating the data has its origins in the EU.


Hopsworks tracks the lineage (or provenance) of ML assets automatically for you. You can see what features are used in which feature view or training dataset. You can see what training dataset was used to train a given model. For assets that are managed outside of Hopsworks, there is support for the explicit definition of lineage dependencies.