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.
Each feature, and so each dataset built using these features, is reproducible, versioned, quality-checked, and searchable.
OUR CUSTOMIZABLE FEATURE STORE
• LOCAL DEV
• PUSH TO GITHUB
• FEATURE PIPELINES
• HIVE/ S3/ DB/ REDIS/...
• 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.
Track utilization of the features along with ownership
SDK and other services to rapidly implement feature engineering modules
Administer versioned, auditable, parameterized pipelines, each generating multiple data sets.
Check provenance of datasets by name or other attributes, and compare runs
Discover datasets via a marketplace for features and along with search interface to build cohorts for analysis
Check drift and access other custom usage monitoring services