The Tecton Feature Store
Build ML features using batch, streaming, and real-time data. Deploy them to production instantly.
Bringing DevOps to ML Data
The Tecton feature store manages data flows for operational ML applications on your cloud infrastructure. It brings the principles of DevOps to the entire feature lifecycle and allows data scientists to build and deploy new features within hours instead of weeks.
Using the Tecton Feature Store
Combine Batch, Streaming, and Real-Time Data
Use all your enterprise data to build high-quality features. Combine batch data (e.g. Amazon S3, Amazon Redshift, Snowflake), streaming data (e.g. Apache Kafka, Amazon Kinesis), and real-time data. Real-time data is passed to Tecton at the time of the feature request, and Tecton executes on-demand transformation to generate real-time values.
Manage features as code
Develop with familiar data science tools
Build features using familiar programming languages and libraries including Python, SQL and PySpark. Use Tecton’s Python SDK in your preferred notebook environment to create training datasets.
Plan feature changes with confidence
“Am I about to modify a feature used in production? How much will this feature cost to process? Is this new feature a duplicate?” Before applying your changes, Tecton generates a plan that allows you to answer these questions. Test changes in private workspaces before deploying them to your production environment, and integrate seamlessly with your existing CI/CD pipeline.
Automate feature transformations
Ensure data consistency
Serve features online
Retrieve feature values from Tecton’s REST/gRPC API to power real-time predictions in production at high scale. Median serving latencies are ~5ms and can easily scale to 10,000s of requests per second.
Monitor data quality and operational metrics
Search and discover features
Built for scale
Built for the Cloud
Choice of deployment models
Learn more about the Tecton Enterprise Feature Store
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