Tecton, a leading provider of feature stores for data scientists and data engineers to manage data sets and data pipelines, has joined hands with Databricks to help organizations build and automate their machine learning (ML) feature pipelines from prototype to production. Tecton is integrated with the Databricks Lakehouse Platform so data teams can use Tecton to build production-ready ML features on Databricks in minutes.
Built on an open lakehouse architecture, Databricks allows ML teams to prepare and process data, streamline cross-team collaboration and standardize the full ML lifecycle from experimentation to production. With Tecton, these same teams can now automate the full lifecycle of ML features and operationalize ML applications in minutes without having to leave the Databricks workspace.
Available on the Databricks Lakehouse Platform, Tecton acts as the central source of truth for ML features, and automatically orchestrates, manages and maintains the data pipelines that generate features. Allowing data teams to define features as code using Python and SQL, the integration further enables ML teams to track and share features with a version-control repository.
Tecton then automates and orchestrates production-grade ML data pipelines that materialize feature values in a centralized repository. From there, users can instantly explore, share and serve features for model training, batch and real-time predictions across use cases without worrying about typical roadblocks such as training-serving skew or point-in-time correctness.