Trustworthy

​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

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

SCRIBBLE ENRICH:

OUR CUSTOMIZABLE FEATURE STORE

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 01 

• LOCAL DEV
• TESTING
• DOCUMENTATION

 02 

• COMMIT
• PUSH TO GITHUB

 03 

• SCHEDULE
• VALIDATE
• OPERATE
• FEATURE PIPELINES

 04 

• STORE
• HIVE/ S3/ DB/ REDIS/...

 05 

• STORE

• DISCOVER
• AUDIT
• COMPARE FEATURES

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.

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.

COMPONENTS

& ARCHITECTURE

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 USECASES 

Track utilization of the features along with ownership

 IMPLEMENT 

SDK and other services to rapidly implement feature engineering modules

 OPERATE 

Administer versioned, auditable, parameterized pipelines, each generating multiple data sets.

 AUDIT 

Check provenance of datasets by name or other attributes, and compare runs

 ACCESS 

Discover datasets via a marketplace for features and along with search interface to build cohorts for analysis

 MONITOR 

Check drift and access other custom usage monitoring services