Collaborative notebooks to train, track, deploy, and monitor ML models

We've started this as an internal project to solve actual problems we have faced delivering machine learning projects for our clients these past few years.

- No-setup notebook environments with the most popular libraries pre-installed

- Real-time collaboration on notebooks

- Multiple versions of your notebooks

- Long-running notebook scheduling with output that survives closed tabs and network disruptions

- Automatic experiment tracking: automatically detects your models, parameters, and metrics and saves them without you remembering to do so or polluting your notebook with tracking code

- Easily deploy your model and get a "REST endpoint" so data scientists don't tap on anyone's shoulder to deploy their model, and developers don't need to worry about ML dependencies to use the models

- Build Docker images for your model and push it to a registry to user it wherever you want

- Monitor your models' performance on a live dashboard

- Publish notebooks as AppBooks: automatically parametrize a notebook to enable clients to interact with it without exporting PDFs or having to build an application or mutate the notebook. This is very useful when you want to expose some parameters that are very domain specific to a domain expert.

Much more on our roadmap. We're only focusing on actual problems we have faced serving our clients, and problems we are facing now. We'd love to hear your thoughts and problems you have faced.