Tecton announced a partnership 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.
"We are thrilled to have Tecton available on the Databricks
Lakehouse Platform," said Adam Conway, SVP of Products at Databricks.
"Databricks customers now have the option to use Tecton to operationalize
features for their ML projects and effectively drive business with production
ML applications."
Productionizing ML models to serve a broad range of predictive
applications including fraud detection, real-time underwriting, dynamic
pricing, recommendations, personalization and search poses unique data engineering
challenges that keep too many organizations from bringing ML to the forefront
of business processes and services. Curating, serving and managing the
predictive data signals that fuel predictive applications, also known as ML
features, is hard. That is why Databricks and Tecton have collaborated to
accelerate and automate the many steps involved in transforming raw data inputs
into ML features and serving those features to fuel predictive applications at
scale.
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.
"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 ML into live, customer-facing applications and business
processes, quickly, reliably and at scale," said Mike Del Balso, co-founder and
CEO of Tecton.
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.
As the interface between the Databricks Lakehouse Platform and
their ML models, Tecton allows customers to process features using real-time
and streaming data from a myriad of data sources. By automatically building the
complex feature engineering pipelines needed to process streaming and real-time
data, Tecton eliminates the need for extensive engineering support and enables
users to drastically improve model performance, accuracy and outcome.