Problem
You have deployed a binary classification model into a live advisor workflow. The challenge is to monitor whether performance holds up after launch and to improve both predictive quality and operational efficiency as data, user behavior, and business constraints change over time.
What You Should Watch
- Core model metrics: accuracy, precision, recall, F1, AUC
- Calibration and predicted versus actual positive rate
- Feature drift and score distribution drift
- Operational metrics such as queue size and advisor capacity usage
Representative Context
A daily batch model scores clients for outreach prioritization in Ameriprise Advisor Center. Scores above a threshold are routed to advisors, so both model quality and threshold choice directly affect business value.
You are practicing as a guest. Sign up free to get your answer graded with AI feedback. Your draft stays right here.
