LOL Love this Thread and sorry I didn't answer earlier!

VivaciousPenguin66 EnviousStarfish54 I totally agree with you. We do have answers to "how do you do X or Y" but we don't have workflows really.

What would be a logical place to start? Would something like "training a Yolo V3 person detector on COCO dataset and how you enable continuous training (let's say adding PASCAL dataset afterwords) be something interesting?

The only problem is the friction between atomic and big picture. In atomic, I'm giving you a step by step guide on how to do things. It's very easy when I use a 100 lines script of pytorch mnist (which also auto-downloads the dataset which weighs like 10MB).
It's harder when it's a GIANT repository with lots of built-in preprocessing and a 20GB dataset.

We do have big-picture blog posts like https://clear.ml/blog/how-theator-built-a-continuous-training-framework-to-scale-up-its-surgical-intelligence-platform/ https://clear.ml/blog/good-testing-data-is-all-you-need-guest-post/ and https://clear.ml/blog/how-trigo-built-a-scalable-ai-development-deployment-pipeline-for-frictionless-retail/ but I feel they are more "philisophical" and thought pieces than actually stuff that you can start working on tomorrow morning.

So my question to you is, what kind of examples would be helpful for you guys?