Week 8 addressed "life after deployment." The model begins creating value the moment it ships, but simultaneously begins to drift from the world. Data changes, bias emerges, systems slow down, performance degrades. This week you learned the entire process of detecting (monitor) and responding to (maintain) these changes. Today we consolidate the four days into one unified picture before the exam.
The big picture is a closed loop: drift/performance/system detection → alarm → retraining → safe redeployment → monitoring again. This loop is the heart of "operational ML (MLOps)."