SAN FRANCISCO, May 19, 2021 (GLOBE NEWSWIRE) -- Tecton, the enterprise feature store company, today announced that it has been recognized as a 2021 Cool Vendor in Enterprise AI Operationalization and Engineering by Gartner, Inc.[1] Tecton emerged from stealth in April last year, and its customers now include startups to the Fortune 50.
Feature stores have emerged as a critical component of the infrastructure stack for ML. They solve the hardest part of operationalizing ML: building and serving ML data to production. They allow data scientists to build better ML features and deploy these features to production quickly and reliably.
“From our experience building the Uber Michelangelo platform, we know that data is the hardest part of production ML systems,” said Mike Del Balso, co-founder and CEO of Tecton. “Our objective is to solve the data problem for ML by making feature stores accessible to every organization. Being named a Gartner Cool Vendor is great validation of the importance of feature stores in the modern MLOps stack.”
Tecton provides the only cloud-native feature store that manages the complete lifecycle of ML features. It allows ML teams to build features that combine batch, streaming and real-time data. Tecton orchestrates feature transformations to continuously transform new data into fresh feature values. Features can be served instantly for training and online inference, with monitoring of operational metrics. Teams can search and discover existing features to maximize re-use across models.
In addition, Tecton is the primary contributor to Feast, an open source feature store that is the fastest path to production for ML data. Feast is the first feature store that can be deployed locally in minutes without dedicated infrastructure. Data scientists can reap the benefits of a functionally complete feature store with no infrastructure overhead or maintenance. Feast has seen strong adoption to date with more than 1,800 GitHub stars.
Last month Tecton hosted apply(), its first ML data engineering conference where data and ML teams discussed the practical data engineering challenges faced when building ML for the real world.
The conference featured speakers from 30 organizations including DoorDash, Etsy, Google, Lemonade, LinkedIn, Microsoft, Netflix, Pinterest, Spotify and Stitch Fix and was attended by thousands of ML practitioners.
Additional Resources
- Read how Atlassian is using Tecton: https://www.tecton.ai/blog/atlassian-accelerates-deployment-of-ml-models-from-months-to-days-with-tecton/
- Read how Tide is using Tecton: https://www.tecton.ai/blog/tide-deploys-models-2x-faster-with-tecton/
- Read the “What is a feature store?” blog: https://www.tecton.ai/blog/what-is-a-feature-store/
About Tecton
Tecton’s mission is to make world-class ML accessible to every company. Tecton enables data scientists to turn raw data into production-ready features, the predictive signals that feed ML models. The founders created the Uber Michelangelo ML platform, and the team has extensive experience building data systems for industry leaders like Google, Facebook, Airbnb and Uber. Tecton is the main contributor and committer of Feast, the leading open source feature store. Tecton is backed by Andreessen Horowitz and Sequoia and is headquartered in San Francisco with an office in New York. For more information, visit https://www.tecton.ai or follow @tectonAI.
[1] Gartner, “Cool Vendors in Enterprise AI Operationalization and Engineering”, Chirag Dekate, Farhan Choudhary, Soyeb Barot, Erick Brethenoux, Arun Chandrasekaran, Robert Thanaraj, Georgia O'Callaghan, 27 April 2021 (report available to Gartner subscribers here: https://www.gartner.com/doc/4001037)
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