This is the direction we're going towards with our internal platform[0] that we built to make our life easier. We do have real-time collaborative notebooks. Ho

This is the direction we're going towards with our internal platform[0] that we built to make our life easier. We do have real-time collaborative notebooks. However, we also have long-running notebook scheduling where notebooks get picked by a runners and executed against certain environments. One cool feature is that you can watch the notebook's output as it runs, even if you had closed the browser or logged in from another computer. Anothr cool feature is that we do automatic experiment tracking, so we detect parameters, models, metrics, and the notebook automatically, removing the cognitive load from the use to "remember" doing that. People rarely remember clerical things, but even when they do, we didn't want to pollute the notebooks with tracking code of say MLflow. This happens automatically. These long-running notebooks are simply jobs. There are other types of jobs: building a Docker image for the model so that you can push it to a registry is also a job, so is deploying a model to get a "REST" endpoint and a live dashboard to monitor model performance. One of our guidelines is: "Anything that is possible with point and click must be possible with an API call", so we make sure to make everything programmable. For example, scheduling a long-running notebook can be done sending in a request. We also are working to add in webhooks and use cloudevents spec so that people can take information from our system, such as a job having finished, and trigger things in other systems.. Another is workload portability. Again, it's still early and we're prioritizing the issues that have frustrated us the most for the past years in our projects. There's a long way to go, especially that there are many such as yourself who may have worked on problems we've never seen, or a scale/data we haven't handled before. - [0]: https://iko.ai https://iko.ai