Which solution will meet these requirements with the LEAST development effort?
Use SageMaker Model Debugger to automatically debug the predictions, generate the explanation, and attach the explanation report.
Use AWS Lambda to provide feature importance and partial dependence plots. Use the plots to generate and attach the explanation report.
Use SageMaker Clarify to generate the explanation report. Attach the report to the predicted results.
Use custom Amazon CloudWatch metrics to generate the explanation report. Attach the report to the predicted results.
Explanations:
SageMaker Model Debugger is primarily used for debugging model training and does not provide explanation capabilities for model predictions. It focuses on identifying issues during model training rather than generating explanations for predictions.
AWS Lambda can be used for various compute tasks but is not specifically designed for generating feature importance or partial dependence plots within the context of SageMaker. This option would require additional effort to implement the explanation generation and attachment process.
SageMaker Clarify is specifically designed for providing explanations for model predictions and can generate reports that include feature importance and fairness metrics. It integrates well with SageMaker, making it the least effort solution for generating and attaching explanation reports to predictions.
Custom Amazon CloudWatch metrics are generally used for monitoring and logging, not for generating explanation reports. This would require significant development effort to create and manage the explanation logic and reporting process outside the scope of the SageMaker ecosystem.
I scheme that the answer is:
Use SageMaker Clarify to generate the explanation report. Attach the report to the predicted results.
If I’m not mistaken, the answer is:
Use SageMaker Clarify to generate the explanation report. Attach the report to the predicted results.