How to develop a successful ML product? Part IV

In today’s presentation by Omar Richardson, we discuss how to monitor performance and use MLOps to maintain prediction quality.

It is important to remember that an ML project is not completed when your model is deployed. Instead of a fixed training set, your model is now exposed to real life data, posing its own challenges. Learn more in the Omar’s presentation.