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You wouldn’t conceive of setting up your own SMS messaging stack across 193 countries and god knows how many telecom carriers in a world where Twilio exists. Machine learning (ML) is in a similar scenario; why would you waste time putting together a whole infrastructure unless Machine Learning is key to your program — which it probably isn’t?
Slai is claiming to have laid the foundation to a developer-first machine learning platform to address this specific challenge. It gives developers the tools they need to release machine-learning apps swiftly. The company’s offering claims to focus on allowing developers to focus on the machine learning models rather than all of the other nonsense that wastes time but doesn’t directly add to the application. You may as well be able to connect a data source to the product. That may be your database or an S3 bucket containing machine learning model data. The machine learning model then identifies predictions in the data using Python code. The company suggests wrapping that in an API that performs things like validating user input or process output before returning it to the user.
A machine learning application is made up of these components. As a result, if someone were to do this by hand, they would have to put up their web server. They’d have to build up a versioning system and a monitoring mechanism for the model. And it all adds up to a lot of busy work. The company claims to take care of everything for the user. They have to worry about where their data comes from and whatever model they’re employing. Everything else is taken care of for them. In a nutshell, all of the glue from the machine learning development process is removed by Slai.
The company advertises three main features:
- Designed for Production
Consistent project structure, testing, CI/CD, and everything a production app requires.
- Version Management
Slai enables source control, making your code and data a single source of truth.
- Pre-configured settings
Never begin a project from the beginning. Logic and environments can be shared between projects.
The startup bills itself as a GitHub for machine learning, making it simple to clone existing machine learning recipes for use in apps. The company recently announced that it raised a $3.5 million seed round, which could be used to better the services offered and build more state-of-the-art machine learning models.