If you’re a data scientist or you work with machine learning (ML) models, you have tools to label data, technology environments to train models, and a fundamental understanding of MLops and modelops.

If you really think about it, a data life cycle is quite difficult to pin down and depending on your industry or profession, the number of agreed steps vary widely. For example, the Harvard Business ...