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Tecton Releases Cloud-Native Enterprise Feature Store for Machine Learning, Raises $35 Million Series B Co-Led by Andreessen Horowitz and Sequoia

Tecton, the enterprise feature store company, today announced the general availability of its production-ready enterprise feature store that is delivered as a fully-managed cloud service. Tecton, which emerged from stealth in April this year, already has paying customers from some of the fastest growing startups to the Fortune 50.

"We use ML applications to support a variety of use cases in Credit Decisioning, Cash Flow Insights, Fraud Detection and Business Admin. Tecton helps us create more accurate features that combine batch data from Snowflake and streaming data from Apache Kafka. With Tecton, we can reuse features across all of these domains and thus reduce by weeks the time it takes to build and deploy streaming features to production," said Hendrik Brakmann, Director of Data Science and Analytics at Tide, a startup that provides a smart business current account to more than 270,000 business owners.

"We operate ML-driven applications to power new and improved customer experiences in our products, including Jira and Confluence. Tecton is now deployed in production as a core component of our stack for operationalizing ML. Tecton has allowed us to improve more than 200,000 customer experiences per day and accelerate the time to build and deploy new features from months to less than a day," said Gilmar Souza, Engineering Manager, Search & Smarts at Atlassian, a global software company that helps teams unleash their potential.

Feature stores are emerging 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 more accurate ML features and deploy these features to production within hours instead of months.

"Based on our experience building Uber Michelangelo, we know that feature stores are an essential part of the complete stack for operational ML," said Mike Del Balso, co-founder and CEO of Tecton. "We built Tecton to provide the most advanced feature store in the industry and make it accessible to every organization as a cloud-native service."

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.

Tecton Raises $35 Million

Today Tecton also announced $35 million in Series B funding co-led by Andreessen Horowitz and Sequoia, bringing the total raised to $60 million. The funding will be used to accelerate product innovation and go-to-market activities.

Martin Casado, general partner at Andreessen Horowitz and Tecton board member, said: "We are generating data at higher volumes and with greater velocity than ever before. ML will drive a new wave of software innovation by allowing companies to harness their data to power new customer experiences and automate business processes. Scaling ML in the enterprise requires new tooling to turn analytics data into operational signals, and the Tecton team is uniquely positioned to solve this problem with their deep expertise in ML infrastructure."

Matt Miller, partner at Sequoia and Tecton board member, said: "Ten years from now, every enterprise system will be automated and driven by machine learning. The core technology that will make this possible is the feature store that Tecton provides. Tecton is the key enabler of operational machine learning and is solving one of the most interesting challenges in enterprise technology. The team has made tremendous progress since the seed, and we are thrilled to triple down on our partnership."

Published Monday, December 07, 2020 12:12 PM by David Marshall
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