Council Post: Want To Measure Your Enterprise AI Initiatives? Start With Model Debt (Part 2 Of 2)

In part one of this discussion, I presented the basic concept of model debt as a way to measure the effectiveness of individual models and AI programs overall. In part two, I'll go through a short example to show how model debt can be computed in practice. The target production days (TPDs), which is a count of the number of days that the model is intended to be in production over its full life cycle, starting from when the data science team releases it for production. The shorter the lock-to-load time, the faster the model can contribute to the business. The actual lock-to-load time will depend on how effectively the ModelOps process moves the model through its life cycle steps as defined by the enterprise AI architect, including technical checks (e.g., security scans, performance verification, etc.), governance requirements (e.g., regulatory compliance, explainability reports, etc.) and business considerations (e.g., agreement on KPIs, departmental sign-offs, etc.).