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In a statement on Thursday, Tecton announced an integration that will make its feature store available on Databricks’ platform, giving joint customers a way to build and automate their ML feature pipelines, from prototype to production, in a matter of minutes.
“Building on Databricks’ powerful and massively scalable foundation for data and AI, Tecton extends the underlying data infrastructure to support ML-specific requirements. This partnership with Databricks enables organizations to embed machine learning into live, customer-facing applications and business processes, quickly, reliably and at scale,” Mike Del Balso, cofounder and CEO of Tecton, said.
How does Tecton feature store accelerate ML application deployment?
For any predictive application to work, the ML model underneath has to be trained on historical data. In most cases, this data can be visualized as a table, with rows representing certain elements and columns providing attributes describing those elements. Each individual attribute, or measurable property, is a called a feature. Data scientists usually apply transformations to raw data to create features for ML models, but the process comes with unique engineering challenges and takes a lot of time, affecting the training and deployment timelines.
A feature store provides data scientists with a dedicated place to save developed features for reuse at a later stage or by another team member within the same organization. Tecton also does the same job, although its offering goes a step ahead and also automates the entire lifecycle of ML ...