Introduce a customer-churn prediction model into production.
Organize data preparation, training, evaluation, deployment, and operations.
Which description of a typical machine learning lifecycle is appropriate?
The lifecycle covers defining the goal, preparing data, training, evaluating, deploying, and monitoring iteratively.
Detailed explanation
Model development is iterative and includes evaluation and operational monitoring.
Model development is iterative and includes evaluation and operational monitoring.
Goals and metrics should be defined before development.
Goals and metrics should be defined before development.
This causes leakage and invalidates the evaluation.
This causes leakage and invalidates the evaluation.
Data and business conditions can change after deployment.
Data and business conditions can change after deployment.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01ガイドのDomain 1.3で、AI/ML開発ライフサイクルの各段階を確認する。Expected result
開発前の目的定義から本番後の監視までを一連のライフサイクルとして説明できる。Key points
- Data preparation
- Separating evaluation data
- Production monitoring
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.