How To Use Digital Strategy KPIs To Justify ML Infrastructure And MLOps

Machine Learning Engineering and MLOps are too granular for senior leaders to understand. That makes it hard to get budget. Using familiar KPIs helps connect team needs with the value they return.

Trying to justify Machine Learning Engineering to senior leadership is difficult. The cost of architecting, buying, building, and supporting Data Science is a significant part of the overall Data and Analytics Organization’s budget. Getting the C Suite to understand that line item is easier when you use a framework they’ve already adopted.

Getting them to sign off on something they don’t have visibility into is an effort in futility. Data Science teams need a way to show senior leaders the ROI. Fortunately, Gartner and the Big 5 Consulting companies are doing some of the heavy lifting for us. We can adapt their KPIs to support ML Engineering and MLOps.