Until recently, companies were challenged with deploying one Machine Learning (ML) model into production. What MLOps needs is a dedicated Model Performance Management (MPM) that acts as a centralized control system at the heart of the ML workflow that tracks and watches the model perfromance at all the stages and closes the ML feedback loop. In this paper, we will briefly introduce the lifecycle of ML, cover some of the wide-known challenges, and discuss how to optimize MLOps with a disciplined Model Performance Management framework.
Download this resource to learn more.
Powered by

By clicking/downloading the asset, you agree to allow the sponsor to have your contact information and for the sponsor to contact you.