Aren't you one of the authors of that paper? It was a good read. Have a look at
https://iko.ai, it solves not only many of the pain points you wrote about, but the "critical" pain points (difficult
and important).
- No-setup collaborative notebooks: near real-time editing on the same notebook. Large Docker images.
- Long-running notebook scheduling: you can schedule a notebook right from the JupyterLab interface and continue to work on your notebook without context switch. The notebook will run and you can view its state even if you close your browser or get disconnected, and you can view it without opening the JupyterLab interface, even on your mobile phone. https://iko.ai/docs/notebook/#long-running-notebooks
- Automatic experiment tracking: iko.ai automatically detects parameters, metrics, and models and saves them. Users don't have to remember or know how to write boilerplate code or experiment tracking code.
- One click parametrization: you can publish an AppBook to enable other people to run your automatically parametrized notebook without being overwhelmed by the interface. You don't have to use cell tags or metadata to specify parameters. You click a button and an application is created from your notebook. The runs of this application are also logged as experiments in case they generate a better model. https://iko.ai/docs/appbook/
- Easy deployment: people can look at the leaderboard, and click on a button to deploy the model they choose into a "REST API" endpoint. They'll be able to invoke it with a POST request or from a form where they simply upload or enter data and get predictions.
We haven't focused on the stylesheets given that in our previous projects with actual paying enterprise customers, it wasn't CSS that held us back.
Here's an invite link that's valid multiple times:
https://iko.ai/invite/lEMzE_hKwJ2SUbfLdnK7SbZb1c3zUCOAQexakL...