Tecton.ai nabs $35M Series B as it releases machine learning feature store

Tecton.ai, the startup founded by three former Uber engineers who wanted to bring the machine learning feature store idea to the masses, announced a $35 million Series B today, just seven months after announcing their $20 million Series A.

When we spoke to the company in April, it was working with early customers in a beta version of the product, but today, in addition to the funding they are also announcing the general availability of the platform.

As with their Series A, this round has Andreessen Horowitz and Sequoia Capital coming back to co-lead the investment. The company has now raised $60 million.

The reason these two firms are so committed to Tecton is the specific problem around machine learning the company is trying to solve. We help organizations put machine learning into production. That's the whole goal of our company, helping someone build an operational machine learning application, meaning an application that's powering their fraud system or something real for them [...] and making it easy for them to build and deploy and maintain," company CEO and co-founder Mike Del Balso explained.

They do this by providing the concept of a feature store, an idea they came up with and which is becoming a machine learning category unto itself. Just last week, AWS announced the Sagemaker Feature store, which the company saw as major validation of their idea.

AWS launches SageMaker Data Wrangler, a new data preparation service for machine learning

As Tecton defines it, a feature store is an end-to-end machine learning management system that includes the pipelines to transform the data into what are called feature values, then it stores and manages all of that feature data and finally it serves a consistent set of data.

Del Balso says this works hand-in-hand with the other layers of a machine learning stack. When you build a machine learning application, you use a machine learning stack that could include a model training system, maybe a model serving system or an MLOps kind of layer that does all the model management, and then you have a feature management layer, a feature store which is us - and so we're an end-to-end lifecycle for the data pipelines," he said.

With so much money behind the company it is growing fast, going from 17 employees to 26 since we spoke in April with plans to more than double that number by the end of next year. Del Balso says he and his co-founders are committed to building a diverse and inclusive company, but he acknowledges it's not easy to do.

It's actually something that we have a primary recruiting initiative on. It's very hard, and it takes a lot of effort, it's not something that you can just make like a second priority and not take it seriously," he said. To that end, the company has sponsored and attended diversity hiring conferences and has focused its recruiting efforts on finding a diverse set of candidates, he said.

Unlike a lot of startups we've spoken to, Del Balso wants to return to an office setup as soon as it is feasible to do so, seeing it as a way to build more personal connections between employees.

Tecton.ai emerges from stealth with $20M Series A to build machine learning platform