Tecton Uses Machine Learning to Better Handle Immense Data

As enterprises grapple with leveraging their abundant data for maximizing competitive advantages, an emerging set of practices, dubbed “MLOps,” is designed to simplify the process of parsing information using machine learning. One MLOps startup—the San Francisco-based Tecton—is proof positive of the value of machine learning models. In July, the company raised $100 million in Series C funding, raising the total of its fundraising to $160 million in less than four years.

Become a Subscriber

Please purchase a subscription to continue reading this article.

Subscribe Now

Tecton’s team of founders, led by Chief Executive Officer Mike Del Balso, launched the company after working together at Uber. While at that company, the trio (rounded out by Jeremy Hermann and Kevin Stumpf) created Michelangelo, an AI platform built to generate market forecasts, automate fraud detection, and more. The product’s success inspired them to create Tecton, which has become an award-winning feature platform that has partnerships with industry-leading companies like Snowflake and Databricks.

On a macro level, Tecton automates the process of building features using real-time data sources, deploying individual independent variables that act like input to make predictions. The company’s latest funding round was led by Kleiner Perkins, with participation from Databricks, Snowflake, Andreessen Horowitz, Sequoia Capital, Bain Capital Ventures, and Tiger Global.