Compare data processing, models, and hyperparameters.
Explain the selected candidate later.
Choose two practices.
Record data, code, settings, seeds, environment, metrics, and time, and compare quality, safety, cost, latency, bias, and adoption rationale.
Detailed explanation
Experiments can be reproduced and compared.
Experiments can be reproduced and compared.
A single metric does not decide the model.
A single metric does not decide the model.
Failure analysis and comparison are lost.
Failure analysis and comparison are lost.
Results are not comparable.
Results are not comparable.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 1.3の実験管理、MLOps、モデル選択を確認する。Expected result
モデル候補の比較条件と採用理由を再現可能な形で管理できる。Key points
- Experiment tracking
- Reproducibility
- Rationale
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.