Feast
Managing data in Feast feature stores.
Feast (Feature Store) is an operational data system for managing and serving machine learning features to models in production. Feast is able to serve feature data to models from a low-latency online store (for real-time prediction) or from an offline store (for scale-out batch scoring or model training).
When would you want to use it?
There are two core functions that feature stores enable:
access to data from an offline / batch store for training.
access to online data at inference time.
COMING SOON: While the ZenML integration has an interface to access online feature store data, it currently is not usable in production settings with deployed models. We will update the docs when we enable this functionality.
How to deploy it?
To use the feature store as a ZenML stack component, you also need to install the corresponding feast
integration in ZenML:
Now you can register your feature store as a ZenML stack component and add it into a corresponding stack:
How do you use it?
Online data retrieval is possible in a local setting, but we don't currently support using the online data serving in the context of a deployed model or as part of model deployment. We will update this documentation as we develop this feature.
Getting features from a registered and active feature store is possible by creating your own step that interfaces into the feature store:
Note that ZenML's use of Pydantic to serialize and deserialize inputs stored in the ZenML metadata means that we are limited to basic data types. Pydantic cannot handle Pandas DataFrame
s, for example, or datetime
values, so in the above code you can see that we have to convert them at various points.
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