I'm not aware of any resources for that specific question. The closest resource I can think of would be "Designing Data Intensive Applications" which is great for designing systems with data integrity that will lend themselves to ML later on. My next recommendation would actually be to work on a Kaggle project (esp the toy starter project https://www.kaggle.com/c/titanic). Looking at Kaggle notebooks, sklearn documentation, etc is really valuable for understanding how to pose problems in a way solvable with ML.

The latest thinking around 'data science as a product' seems to take this approach. See Building Machine Learning Powered Applications for one resource https://www.oreilly.com/library/view/building-machine-learni...

Great reference. Thanks.