Monitoring AI Over Time and Managing Shadow AI
Explore how to monitor AI solutions post-deployment to detect model drift and maintain accuracy. Understand managing shadow AI by creating transparent tool policies and ownership structures. This lesson teaches ongoing operational strategies to ensure AI solutions remain effective, compliant, and aligned with business needs.
We'll cover the following...
A deployed AI solution is not finished when it launches. In the complaint backlog case, complaint categorization accuracy can decline six months after a new product line changes the language, issues, and categories appearing in customer complaints. At the same time, employees may paste customer data into a free online chatbot to work around slow or unclear approved options. These are separate risks, but both require ongoing operational ownership.
Monitoring for model drift
Model drift is the decline in a model’s performance that occurs when the real-world patterns an AI model receives no longer resemble the patterns represented in its training data, a change known as data drift. A new product line, seasonal demand, policy change, or shift ...