A machine learning workflow can involve many steps with dependencies on each other, from data preparation and analysis, to training, to evaluation, to deployment, and more. It’s hard to compose and track these processes in an ad-hoc manner—for example, in a set of notebooks or scripts—and things like auditing and reproducibility become increasingly problematic.
Source: Original Postroducts/ai-machine-learning/using-remote-and-event-triggered-ai-platform-pipelines/">Using remote and event-triggered AI Platform Pipelines