Stu Bailey, Forbes – June 30, 2020
There’s a growing awareness of the widening gap between the ability of data scientists to create models and the ability to deploy them in production. This has driven growing interest in ModelOps, which is the enterprise-wide discipline that enables organizations to scale and govern their AI initiatives by managing models and their life cycles from creation through retirement. But until now, there have been few objective metrics that organizations can use to gauge the effectiveness of their ModelOps programs, or indeed their AI initiatives. The concept of model debt helps to address that need.
