A customer-support classifier is missing more cases after customer behavior changes.
Decide whether reassessment or retraining is needed.
Choose two appropriate monitoring signals.
Monitor changes in input distribution and performance after ground-truth labels become available.
Detailed explanation
Input drift can indicate that production no longer resembles training data.
Input drift can indicate that production no longer resembles training data.
Accuracy, recall, or error can be measured once outcomes are known.
Accuracy, recall, or error can be measured once outcomes are known.
A file name does not show drift or performance.
A file name does not show drift or performance.
Production data and conditions can change over time.
Production data and conditions can change over time.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01ガイドのDomain 3.4でデータドリフト・モデル監視を確認する。Expected result
入力の変化と実績性能を別の監視対象として説明できる。Key points
- Data drift
- Performance monitoring
- Reassessment
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.