New labeled data arrives daily.
Monitor degradation and retrain when justified.
Choose two operational practices.
Validate data quality and distribution before retraining, then use fixed tests, production metrics, approval, and staged rollout for updates.
Detailed explanation
Broken data is not used to update the model.
Broken data is not used to update the model.
Retraining regressions can be managed.
Retraining regressions can be managed.
Quality and safety can regress.
Quality and safety can regress.
Results cannot be reproduced.
Results cannot be reproduced.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 1.3の再学習、モデルレジストリ、評価を確認する。Expected result
定期再学習を自動化しつつ、品質ゲートと承認を残せる。Key points
- Retraining
- Quality gate
- Approval
Notes
- Environment: AWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
- Command output formatting can vary slightly by distribution or tool version.
- Run the example in a temporary directory or process when possible.
Foundation review
Read the scope first
Check whether the command acts on the current shell, a new process, an existing process, or a file.
Verify the observable result
Use the supplied command and compare the output with the expected result.