Recieve the latest from Featureform
Featureform makes data management for machine learning easy. Our Python API enables data scientists to define datasets, transformations, features, and training sets. Drawing inspiration from Terraform, it promotes a declarative approach where every data pipeline, from raw input to processed feature, is transparent and traceable. It operates seamlessly on your existing infrastructure including Databricks and Snowflake. Featureform fosters a robust, versioned, and lineage-rich data workflow for machine learning.
Featureform serves as a collaborative repository, enabling easy access and sharing of datasets and features through APIs and a user-friendly dashboard. The platform offers robust search, monitoring, and lineage tools, allowing users to efficiently discover and understand the characteristics of datasets and features. It ensures immutability of versions, fostering consistent, reliable collaboration and model building across data science teams.
Featureform simplifies the process for data scientists to define real-time, bath, or on-demand features, handling technical complexities like backfill and materialization into stores like DynamoDB or Redis. Its backfill capabilities ensure historical integrity of datasets, while point-in-time correctness guarantees accurate model training and predictions. This empowers teams to deploy sophisticated features with minimal effort, focusing on innovation rather than infrastructure.
@ff entity class User:
last_purchase = ff.FeatureStream(offline_store=snowflake,
online_store=redis, type=ff.Float32)
client write_feature(User. last_purchase, (user_id,
new_value) )
@ff .ondemand_feature()
def ondemand_percent(client, params, entities) :
import featureform as ff
return params[ "TransactionAmount"] /
client features(I("balance", ff.get_run())],
entities=entities)
Featureform integrates with identity providers like Okta and various data catalogs for robust governance and access control, synchronizing with these systems to maintain a single source of truth. It offers granular access controls for precise management of data and feature access, aligning with an organization's security protocols. This approach supports a seamlessly compliant machine learning workflow, knowing that compliance, privacy, and proper access are all in check with your other systems.
See what a virtual feature store means for your organization.