Data, not code, will dictate systems of the future, says Tecton.ai
Tecton.ai was founded by members who created Uber Inc.’s Michelangelo, an end-to-end workflow that enables internal teams to seamlessly build, deploy and operate machine-learning solutions at scale. Through the lessons learned at Uber, the founders of Tecton branched out to create a world-class data platform for machine learning accessible to every company.
So why did this appeal so much to investors like Andreessen Horowitz? Because while data is the future, wrangling data is still one of the most complex tasks that organizations and data scientists can do. And tools that incorporate machine learning must continue to be developed in order to help enterprises understand the overwhelmingly vast world of data.
“I actually think this is probably the biggest shift certainly I’ve seen in my career,” said Martin Casado (pictured, left), general partner at Andreessen Horowitz. “It used to be if you looked at a system … you wrote bad code, you made bugs, you had vulnerabilities in your code — that would dictate the system. But more and more, that’s actually not the case. You create these models, you feed the data models, the data gives you output, and … your workflows around those models are really dictating things.”
Casado and Mike Del Balso (pictured, right), co-founder and chief executive officer of Tecton, spoke with Stu Miniman, host of theCUBE, SiliconANGLE Media’s livestreaming studio, during a digital CUBE Conversation. They discussed Tecton’s future, machine learning, and the importance of the data industry. (* Disclosure below.)
Picturing the future of systems through the eyes of data, not code
The importance of data can’t be overstated, according to Casado. “I honestly think the data industry is going to be 10 times the computer industry,” he said. “With compute, you’re building houses from the ground up, and there’s a ton of value there. With data … you’re extracting insight and value from the universe, right? It’s like the natural system.”
In 2020, 90% of business professionals and enterprise analytics say data and analytics are key to their organization’s digital transformation initiatives, according to a recent study by Acute Market Reports. Both Casado and Del Balso believe that Tecton has a chance to be a very pivotal company in democratizing access to data. The opportunity is enormous because data is still hard to capture, clean up, and interpret in effective ways. In fact, almost three-quarters (73.5%) of recent survey respondents said they spend 25% or more of their time managing, cleaning, and/or labeling data, according to an Appen Ltd. whitepaper. And the demand for data scientists increased 32% in 2019 compared to the previous year, according to a Dice Tech Jobs report released in February.
“What we don’t really know is, how do you take data and reign it in so you can use it in the same way that you use software system?” Casado stated. “Talking about things like data network effects and extracting data is a little bit preliminary, because we still actually don’t even understand … how much work it takes to mine insights from data. So I think that we’re now in this era building the tooling that is required to extract the insights of that data. And I think that’s a very necessary step, and this is where a Tecton comes in to provide that tooling.”
Tecton is a data platform for machine learning that manages all the feature data and transformations to allow an organization to share predictive signals across use cases and understand what they are, according to Del Balso. During their time with Uber, Del Balso and the other founders of Tecton recognized that a feature management layer was the component that really allows a company to scale out machine learning across a number of different use cases, and allows individual data scientists to own more than just one model in production.
“In a machine-learning application, there’s fundamentally two components, right? There’s a model that you have to build that’s going to make the decisions given a certain set of inputs, and then there’s the features, which end up being those inputs that the model uses to make the decision,” Del Balso explained. “And common machine-learning infrastructure stats really are split into two layers. There’s a model management layer and a feature management layer, and that’s an emerging pattern in some of the more sophisticated machine-learning stacks that are out there.”
At the core of Tecton’s strategy are a few simple components. The first is feature pipelines, which are data pipelines that plug into a business’ raw data and turn them into features with predictive signals. The second part of that is a feature store, which catalogs these pipelines and draws the output feature data. The third component is feature service and making data accessible to a data scientist when they’re building their models so they can make these decisions, which is sometimes needed in milliseconds for real-time decisioning.
“We’re at private beta with a number of customers,” Del Balso said. “We are spending time engaging in … deep, hands-on engagements with different teams who are really trying to set up their machine learning on the cloud, figuring out how to get their machine learning in production. And it tends to be teams that are trying to really use machine learning for operational use cases — really trying to drive real business decisions and power their product customer experiences.”
Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s CUBE Conversations.
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