Enrich
 

Our Customizable Feature Store

Trustworthy

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​Our feature store implementations are geared for efficiency and trust in datasets, so teams can deploy more models, faster, and with more confidence in the underlying data.

​Versatile

Each feature, and so each dataset built using these features, is reproducible, versioned, quality-checked, and searchable.​

Quick

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Faster re-training or debugging, and quicker turnaround time, for each new version of each model.

How It Works

Enrich handles the complexity of computation and data semantics by providing a python SDK to develop, document and test the feature engineering modules (transforms, pipelines, scheduling, etc) and controlled execution on the server-side. 

The server provides an interface to discover, operate and audit the resulting features or datasets.

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Hooks at either end of Enrich allow for understanding (cataloguing) input data stores, and surfacing features at any frequency through APIs for downstream consumption, by defining data contracts and integration points. 

So for Data Scientists, the Enrich feature store experience simplifies, standardizes, and speeds up the machine learning model deployment pipeline, with confidence in their performance.