Yes! We want to make the whole ML process from training to deployment to monitoring radically more convenient. The CLI interface makes it easy to quickly train

Yes! We want to make the whole ML process from training to deployment to monitoring radically more convenient. The CLI interface makes it easy to quickly train ML models without ever opening a Jupyter notebook, learning what pandas and numpy are etc. And after you’re done training, we want you to be able to incorporate your models into your existing stack: we have language libraries for programming languages traditionally underserved by the current ml tools: ruby, golang, elixir, nodejs, rust. (We also have python). All prediction happens in process with no network requests, meaning you don’t have to deploy a separate service to host your ml models :)