Setting up a fully automated machine learning pipeline is a must for everyone who wants to scale their ML product successfully. But how are we closing the feedback loop from storage over training to deployment? It takes several steps to get there. This talk is for you if you are interested in any of the points below: 👉 Selecting data for model training in the best possible way 👉 Monitoring models in production and detecting drift 👉 Smart data mining from production serving data (edge or cloud) 👉 Managing training and test datasets to prevent data leakage 👉 What’s the benefit of a feedback loop?