Start with ML math and neural basics before architecture debates.
Build data-pipeline discipline before trusting model gains.
Learn compute and accelerator constraints before silicon conclusions.
Close with EDA workflow and deployment governance for production readiness.
METRIC -> MECHANISM -> ARTIFACT -> OWNER -> DECISION
1. Which engineering metric moved and on which workload slice?
2. Which mechanism explains the movement?
3. Which artifact proves the hypothesis?
4. Which owner can apply minimal reversible fix?
5. What validation + rollback gate closes risk?