Dec
07
2020
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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 co-leading 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.

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 life cycle 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.

Apr
28
2020
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Tecton.ai emerges from stealth with $20M Series A to build machine learning platform

Three former Uber engineers, who helped build the company’s Michelangelo machine learning platform, left the company last year to form Tecton.ai and build an operational machine learning platform for everyone else. Today the company announced a $20 million Series A from a couple of high-profile investors.

Andreessen Horowitz and Sequoia Capital co-led the round with Martin Casado, general partner at a16z and Matt Miller, partner at Sequoia joining the company board under the terms of the agreement. Today’s investment combined with the seed they used to spend the last year building the product comes to $25 million. Not bad in today’s environment.

But when you have the pedigree of these three founders — CEO Mike Del Balso, CTO Kevin Stumpf and VP of Engineering Jeremy Hermann all helped build the Uber system —  investors will spend some money, especially when you are trying to solve a difficult problem around machine learning.

The Michelangelo system was the machine learning platform at Uber that looked at things like driver safety, estimated arrival time and fraud detection, among other things. The three founders wanted to take what they had learned at Uber and put it to work for companies struggling with machine learning.

“What Tecton is really about is helping organizations make it really easy to build production-level machine learning systems, and put them in production and operate them correctly. And we focus on the data layer of machine learning,” CEO Del Balso told TechCrunch.

Image Credit: Tecton.ai

Del Balso says part of the problem, even for companies that are machine learning-savvy, is building and reusing models across different use cases. In fact, he says the vast majority of machine learning projects out there are failing, and Tecton wanted to give these companies the tools to change that.

The company has come up with a solution to make it much easier to create a model and put it to work by connecting to data sources, making it easier to reuse the data and the models across related use cases. “We’re focused on the data tasks related to machine learning, and all the data pipelines that are related to power those models,” Del Balso said.

Certainly Martin Casado from a16z sees a problem in search of a solution and he likes the background of this team and its understanding of building a system like this at scale. “After tracking a number of deep engagements with top ML teams and their interest in what Tecton was building, we invested in Tecton’s A alongside Sequoia. We strongly believe that these systems will continue to increasingly rely on data and ML models, and an entirely new tool chain is needed to aid in developing them…,” he wrote in a blog post announcing the funding.

The company currently has 17 employees and is looking to hire, particularly data scientists and machine learning engineers, with a goal of 30 employees by the end of the year.

While Del Balso is certainly cognizant of the current economic situation, he believes he can still build this company because he’s solving a problem that people genuinely are looking for help with right now around machine learning.

“From the customers we’re talking to, they need to solve these problems, and so we don’t see things slowing down,” he said.

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