| Could you give examples of where exactly in the ML process/lifecycle you're hitting these issues? For example: "When training a [type] model with X characteristics, the GIL causes Y, which makes it impossible to do Z". We're building our machine learning platform[0] to solve problems we have faced shipping ML products to enterprise, and are interested in your problems as well. For example, we've faced the environment/dependencies/"runs on my machine" problems and have addressed these with Docker images. Our users can spin up a notebook server with near real-time collaboration to work with others, and no setup because the environment is there. The same with training jobs: they can click on a button and schedule a long-running notebook that runs against a specific environment to avoid "just yesterday I had X accuracy on my machine". The runs are tracked, the models, parameters, and metrics are automatically tracked because if we rely on a notebook author to do it, they might forget or have to context switch and it's an added cognitive load. Some problems we faced were during deployment, too, where a "data scientist" writes a notebook to train a model and then we had to deploy that model reading their notebook or looking into dependencies. Now they can click on a button and deploy whichever model they want. It really was hindering us because they were asking someone else's help, who may have been working on something else. - [0]: https://iko.ai |