At Snowflake, we enable customers to achieve their business missions through the use of data at scale with Snowflake’s Data Cloud and the technology and services of our ecosystem of partners. Together with our partners, we enable organizations across industries, and teams across business units, to operate more efficiently, effectively and collaboratively.
Advances in machine learning mean that organizations can use data insights to drive real-time decision-making as well as improvements in customer experience and business processes. To meet those increased organization-wide demands for data insights, data science teams must be able to scale and speed up the number of machine learning models they can get into production. Many data scientists struggle to move as quickly as needed since they lack a modular approach to machine learning which reuses existing features. Instead, they treat each new machine learning use case as custom work resulting in inefficiency and duplicated effort, resulting in many models never moving forward into production.
Snowflake Ventures is excited to announce our investment in Tecton, a leading enterprise feature platform provider. Functioning as a central hub of the data input signals that machine learning models use to make predictions, a feature platform allows data scientists to automate, manage, and share the datasets and data pipelines required to put models into production. Tecton was also named Snowflake’s Emerging Technology Partner of the Year at our Snowflake Summit in June.
Through our partnership, and Tecton’s native integration with the Data Cloud, Tecton offers data scientists a seamless user experience regardless of whether they’re transforming data for batch, streaming, or real-time processes. Snowflake and Tecton help abstract infrastructure complexity for data scientists working with operational machine learning use cases where applications use machine learning to autonomously and continuously make real-time business decisions. Examples include recommendation systems, search ranking, and dynamic pricing.
For example, Tide, a UK business banking provider for small and medium enterprises, uses the integration between Snowflake and Tecton to build and reuse machine learning features across its organization for lending, risk, and real-time fraud detection applications.
Data scientists can take advantage of Tecton’s native integration with Snowflake to propagate the Data Cloud’s scalability, security and governance across first- and second-party data-as-features sharing. Data scientists can also build features on top of third-party provider datasets available in the Snowflake Marketplace such as weather and public financial information.
For more information on our investment framework, visit the Snowflake Ventures website here.