Set and Don’t Forget

It’s no longer rare to see machine learning (ML) models being used to support a variety of business decisions, from whether a medical claim should be paid or sent to the fraud investigation team, to what route will be more efficient for a delivery truck or what discount should be offered to a distributor. But while these ML-based solutions can be powerful tools to improve core operations, a significant number of organizations still consider the job done when the model is installed and running, unaware of the risks that this “set and forget” approach creates for the business. <h1>Consistent monitoring is key</h1> In practice, without robust post-deployment monitoring, the gains extracted from predictive models are likely to be short-lived. To understand why that happens, imagine a company using an ML model to recommend personalized retention offers to individual customers. The model goes through a rigorous test of accuracy that shows an increase from 75% to 90% in the customer retention rate. The new model is deployed, the promised results are achieved, and everybody is happy. <a href="https://medium.com/slalom-data-ai/set-and-dont-forget-73de2f81b302">Click Here</a>