featureform

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What is Featureform?

Featureform is a virtual feature store. It enables data scientists to define, manage, and serve their ML model's features. Featureform sits atop your existing infrastructure and orchestrates it to work like a traditional feature store. By using Featureform, a data science team can solve the following organizational problems:

• Enhance Collaboration Featureform ensures that transformations, features, labels, and training sets are defined in a standardized form, so they can easily be shared, re-used, and understood across the team.
• Organize Experimentation The days of untitled_128.ipynb are over. Transformations, features, and training sets can be pushed from notebooks to a centralized feature repository with metadata like name, variant, lineage, and owner.
• Facilitate Deployment Once a feature is ready to be deployed, Featureform will orchestrate your data infrastructure to make it ready in production. Using the Featureform API, you won't have to worry about the idiosyncrasies of your heterogeneous infrastructure (beyond their transformation language).
• Increase Reliability Featureform enforces that all features, labels, and training sets are immutable. This allows them to safely be re-used among data scientists without worrying about logic changing. Furthermore, Featureform's orchestrator will handle retry logic and attempt to resolve other common distributed system problems automatically.
• Preserve Compliance With built-in role-based access control, audit logs, and dynamic serving rules, your compliance logic can be enforced directly by Featureform.

Further Reading

• Feature Stores Explained: The Three Common Architectures

In reality, the feature’s definition is split across different pieces of infrastructure: the data source, the transformations, the inference store, the training store, and all their underlying data infrastructure. However, a data scientist will think of a feature in its logical form, something like: “a user’s average purchase price”. Featureform allows data scientists to define features in their logical form through transformations, providers, labels, and training set resources. Featureform will then orchestrate the actual underlying components to achieve the data scientists' desired state.

How to use Featureform

Featureform can be run locally on files or in Kubernetes with your existing infrastructure.

Kubernetes

Featureform on Kubernetes can be used to connect to your existing cloud infrastructure and can also be run locally on Minikube.

To check out how to run it in the cloud, follow our Kubernetes deployment.

To try Featureform in a single docker container, follow our docker quickstart guide

Contributing

• To contribute to Featureform, please check out Contribution docs.
• Welcome to our community, join us on Slack.

Report Issues

Please help us by reporting any issues you may have while using Featureform.

License

• Mozilla Public License Version 2.0