Uber Pals Launch Data Platform for Machine Learning

Three former Uber employees jumped ship last year to launch their own vessel.

Now afloat on $25 million in seed and Series A funding, emerged from stealth mode today and formally unveiled its namesake data platform for machine learning (ML).

The startup's founders, Mike Del Balso (CEO), Kevin Stumpf (CTO) and Jeremy Hermann (vice president of engineering), worked together at the rideshare company and built Uber's Michelangelo, an internal platform for building, deploying and managing ML systems in production, Del Balso explained in a blog post.

"We worked with dozens of teams tackling hundreds of business problems with ML," Del Balso wrote, "from user experiences like ETA prediction to operational decisions like fraud detection. Across these efforts, we kept seeing the same pattern: data science teams would build promising offline prototypes, but would end up taking months (or quarters) of back-and-forth with engineering to actually get things fully 'operationalized' and launched in production."

Tecton is an enterprise-ready data platform built specifically for ML. It expands on the ideas behind Michelangelo's "feature store," which managed a shared catalog of vetted, production-ready feature pipelines ready for teams to import and deploy in their own solutions. Tecton was designed to accelerate and standardize the data workflows that support the development and deployment of ML systems, with the goal of improving time-to-value, increase ROI on ML efforts and "fundamentally change how ML is developed within data science organizations."

"From model training to launch, the feature store changed how ML projects were developed at Uber," Del Balso said. "It introduced standardization, governance, and collaboration to previously disparate and opaque workflows. It unified the feature engineering process with the production serving of those features. The result was faster development, safer deployment, and an exponential increase in the number of ML projects running in production."

Needless to say, the Tecton platform includes a feature store of its own. The platform also includes feature pipelines for transforming raw data into features or labels; a feature server for serving the latest feature values in production; an SDK for retrieving training data and manipulating feature pipelines; a Web UI for managing and tracking features, labels and data sets; and a monitoring engine for detecting data quality or drift issues and alerting.

The $25 million in seed and Series A funding was co-led by Andreessen Horowitz and Sequoia. Both Martin Casado, general partner at Andreessen Horowitz, and Matt Miller, partner at Sequoia, have joined the startup's board.

"The founders of Tecton built a platform within Uber that took machine learning from a bespoke research effort to the core of how the company operated day-to-day," Miller said in a statement. "We believe their platform for machine learning will drive a Cambrian explosion within their customers, empowering them to drive their business operations with this powerful technology paradigm, unlocking countless opportunities."

About the Author

John K. Waters is the editor in chief of a number of sites, with a focus on high-end development, AI and future tech. He's been writing about cutting-edge technologies and culture of Silicon Valley for more than two decades, and he's written more than a dozen books. He also co-scripted the documentary film Silicon Valley: A 100 Year Renaissance, which aired on PBS.  He can be reached at