Ok, I'll bite. disclaimer: this is a nice framework, will happily try it. Imho: The underlying patterns are quite clear, and there are various approaches to b

Ok, I'll bite. disclaimer: this is a nice framework, will happily try it. Imho: The underlying patterns are quite clear, and there are various approaches to build stable pipelines. I have used automation with basic containers + gitlab actions / custom runners, clearml, earthly pipelines, kubeflow,.. for this. All of those can give reproducibility (experiment tracking, code & dependencies, etc.) without much effort. The last mile (model deployment) is often very specific, so let's keep that out of scope. But: The basic problem is cultural, not technical. One stated goal of this project hits close to the root cause: "Facilitating the transition from Jupyter Notebook prototype code to steady production-grade pipelines". As ML developers, we have to stop regarding notebooks as anything that produces acceptable output (apart from initial exploration). Work has to happen in structured, tracked, and versioned codebases (~production code). Anything that happened locally/in a notebook might as well not exist from my point of view.