JealousParrot68 Some usability comments - Since ClearML is opinionated, there are several pipeline workflow behaviors that make sense if you use Datasets and Artefacts interchangeably, e.g. the step caching AgitatedDove14 mentioned. Also for Datasets, if you combine them with a dedicated subproject like I did on my show, then you have the pattern where asking for the dataset of that subproject will always give you the most up-to-date dataset. Thus you can reuse your pipelines without having to know exactly which version should be used "right now". This embodies our ideal of "decoupling code from data".

Re: boilerplate / fluidity in roles - I think this is what makes ClearML shine for R&D workflows. We can't hope to guess exactly how everyone's MLOps is taking shape, but we can help you get what you need with the fewest lines of code possible.
What my show is aiming to convey at that arc is that you can quickly build on top of our abstractions the functionality that suits you the best. As usually occurs, should you find that you are writing the same code over and over - you could always refactor that out (and maybe submit a nice PR? 😍 ).

Hope this helps a bit as well. If there is anything you'd like to see me going over in the show, let me know 😉