We make it possible for Data Scientists and Engineers to spend their time on hard and creative ML tasks by using a common platform to build, deploy and manage ML models. And easily leveraging the cloud (on-prem or public).
ML Studio is built around a few open-source tools your already use and other tools we build ourselves to make your life easier:
None of the tools we built are designed to lock you in.
If you are already using Git then you are going to fit right in because every change is tracked in Git automatically. Individual deployment configuration is tracked inside your Git repo under .mlstudio/app.db, it is SQLite database. And artifacts are stored in /home/mlstudio/models directory so together with the related deployment configuration you can reproduce the same results and review the code that leads to them.
All that is open because we do not want to lock you in, we believe that after you try ML Studio you will be hooked. And if you ever feel want to leave you know that all your work and deployments configuration are yours and that you will be taking them with you.
Message usStay on top of your hardware utilization and review your models deployments.
Mobile and tablet friendly designs. Monitor and manage your deployments on the go.
ML Studio is a Kubernetes application. You can run it in a new cluster or deploy it to your existing cluster.
Your work, your data, you are in full control. We will not have to learn anything new and if you decide to leave, you are free to go at anytime.
Features like user calibration tools, different deployment testing strategies, drift detection alerts, and 1-click app installs (apps like AirFlow, Meltano, etc...).
ML Studio is free during Early Access Program (EAP). EAP users have full access to all the product features.
$150 monthly per installation. A good Laptop for Machine Learning costs about $2,000 so we charge under that yearly per installation not per user and we offer features like NoOps deployments and eventually testing (A/B, canary, ...) on top of that.
Prices might be adjusted before public release.
If you are a Data Scientist, you probably spend more time on engineering, DevOps, switching between local and cloud environments, and depending on other teams to get your work into production than you should or want to.
ML is hard and we want to help you focus on solving interesting problems instead of worrying about the tools you use and other inconveniences.
We build ML Studio for you to shorten your workflow and make it more enjoyable.
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