Build ML features using batch, streaming, and real-time data. Deploy them to production instantly.
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.
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.
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.
“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.
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