Slai makes intelligent choices around machine learning setups for developers – TechCrunch

In a world where Twilio exists, you wouldn’t dream of standing up to your own SMS messaging stack across 193 countries and god-knows how many telcom operators. The situation for machine learning (ML) is not the most dissimilar; Unless ML is the core of your software – and it’s probably not – why would you waste an entire infrastructure on assembling. To solve that exact issue, Slai is building a developer-first platform for machine learning. It equips developers with the tools to quickly ship machine-learning applications.

“Today, machine learning remains a research discipline, and it’s still very hard for a developer to build their own machine learning application,” shares Eli Mernit, co-founder and CEO at Slai. “Our hope is that developers are empowered to build state-of-the-art machine learning models.”

The company raised today a $ 3.5 million seed round led by Tiger Global, with additional investment from Y Combinator, Charge Ventures, Uncorrelated Ventures, Twenty Two Ventures and Soma Capital, along with angels Guy Podjarny and Jason Warner.

The company’s product is focused on letting developers focus on machine learning models, rather than on the kerfuffle that takes a lot of time, but not directly on the application itself.

Action in Screenshot of Slai.ai. Image credit: Slai.

“The product lets you connect to a data source. That could be your database or an S3 bucket with data that you want to send to a machine learning model. And then the machine learning model – just some Python code – finds predictions in the data. We’ve wrapped that in an API, that does things like validate the input on the user passes, or does some processing on the output before sending it back to the user, ”says Mernit. “Those components are a machine learning application. And if so, if someone was doing this stuff by hand, they would have set up a web server. They would have set some versioning system, they would have set up some kind of monitoring model. And all of this is a lot of busy work. We’re all for the user. All they have is a focus on where their data is coming from and what type of model they are using. The rest is handled for them. In a nutshell, we eliminate all the glue that goes into the machine learning development process. ”

The platform thinks of itself as GitHub for ML – and makes it easy to use existing recipes for machine learning.

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