Production accuracy has decreased.
Investigate input distribution changes and label quality separately.
Choose two investigation steps.
Compare production and training inputs, missingness, units, and category ratios, and separately check label delays, errors, definition changes, and model versions.
Detailed explanation
These checks can reveal data drift.
These checks can reveal data drift.
Quality causes can be separated from input drift.
Quality causes can be separated from input drift.
The cause may be data or labels rather than the model.
The cause may be data or labels rather than the model.
A root-cause investigation is missing.
A root-cause investigation is missing.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 1.2のデータドリフト、ラベル品質、モデル監視を確認する。Expected result
モデル劣化をデータ・ラベル・モデルの仮説に分解して調査できる。Key points
- Drift
- Label quality
- Root cause
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.