Applied ML in an Agentic World
Turn a model score into a decision an agent can defend. Design the policy for a grocery assistant that recommends, routes to review, or abstains.
Inside the course
- Calibrate confidence against observed outcomes.
- Allocate scarce review slots by expected error cost.
- Evaluate retrieval against hard shopper constraints.
- Write and test a release policy with six capstone cases.
Try the free calibration lab →
Course enrollment
$999 one-time payment
Includes the downloadable field workbook, worked examples and decision-policy capstone.
Join the launch listGet enrollment updates
Leave your email for course enrollment updates from Allan.