Deploying Real-Time Machine Learning Applications with Databricks

Databricks has partnered with Tecton to help organizations build and automate machine learning feature pipelines from prototype to production. The integration enables data engineers and data scientists to create production-ready feature pipelines across batch, streaming, and request-time data, with only a few lines of code. Tecton's feature platform allows users to define features as code using Python and SQL, track and share features with a version-control repository, and process these features using real-time and streaming data from various sources. The platform automates the complex process of transforming raw data into features used to train ML models and feed predictive applications in production. With Tecton, data scientists can train models using historical features without worrying about point-in-time correctness or consistency with model serving, while data engineers can ensure that features are served only the latest ones while maintaining high scale, high freshness, and low latency.