In a world the place Twilio exists, you wouldn’t dream of standing up your personal SMS messaging stack throughout 193 nations and god-knows what number of telcom operators. The state of affairs for machine studying (ML) isn’t totally dissimilar; except ML is core to your software program — and it most likely isn’t — why would you waste time on assembling a complete infrastructure. To unravel that exact concern, Slai is constructing a developer-first platform for machine studying. It equips builders with the instruments to rapidly ship machine-learning functions.
“At present, machine studying stays a analysis self-discipline, and it’s nonetheless very onerous for a developer to construct their very own machine studying utility,” shares Eli Mernit, co-founder and CEO at Slai. “Our hope is that builders are empowered to construct state-of-the-art machine studying fashions.”
The corporate at the moment introduced it raised a $3.5 million seed spherical led by Tiger World, with further funding from Y Combinator, Cost Ventures, Uncorrelated Ventures, Twenty Two Ventures and Soma Capital, together with angels together with Man Podjarny and Jason Warner.
The corporate’s product is concentrated on letting builders concentrate on the machine studying fashions, relatively than on all the encircling kerfuffle that takes up numerous time, however doesn’t instantly contribute to the applying itself.
“The product enables you to join a knowledge supply. That might be your database or an S3 bucket with knowledge that you simply need to ship to a machine studying mannequin. After which the machine studying mannequin — just a few Python code — finds predictions within the knowledge. We’ve wrapped that in an API, that does issues like validation on the enter that the person passes in, or does some processing on the output earlier than it’s despatched again to the person,” explains Mernit. “These elements represent a machine studying utility. And so usually, if somebody was doing these items by hand, they must arrange an online server themself. They must arrange some versioning system, they must arrange a way of monitoring the mannequin. And all of this quantities to numerous busywork. We do all that for the person. All they must concentrate on is the place their knowledge is coming from and what kind of mannequin are they utilizing. The remainder is dealt with for them. In a nutshell, we get rid of all of the glue that goes into the machine studying growth course of.”
The platform thinks of itself as GitHub for ML — and makes it simple to fork present recipes for machine studying to be used in functions.
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