SageMaker Clarify
These examples provide an introduction to SageMaker Clarify which provides machine learning developers with greater visibility into their training data and models so they can identify and limit bias and explain predictions.
SageMaker Clarify Processing
- Fairness and Explainability with SageMaker Clarify
- Fairness and Explainability with SageMaker Clarify - JSON Lines Format
- JSON Support with SageMaker Clarify
- Fairness and Explainability with SageMaker Clarify - Bring Your Own Container
- Fairness and Explainability with SageMaker Clarify - Spark Distributed Processing
- Fairness and Explainability with SageMaker Clarify using AWS SDK for Python (Boto3)
- Explainability with SageMaker Clarify - Partial Dependence Plots (PDP)
- Fairness and Explainability with SageMaker Clarify - Bias Detection With Predicted Label and Facet Datasets
- Explaining text sentiment analysis using SageMaker Clarify
- Explaining Image Classification with SageMaker Clarify
- Explaining Object Detection model with Amazon SageMaker Clarify
- Credit risk prediction and explainability with Amazon SageMaker
SageMaker Clarify Model Monitoring
- Amazon SageMaker Clarify Model Monitors
- Amazon SageMaker Clarify Model Bias Monitor for Batch Transform
- Amazon SageMaker Clarify Model Explainability Monitor for Batch Transform
- Amazon SageMaker Clarify Model Bias Monitor - JSON Lines Format
- Amazon SageMaker Clarify Model Explainability Monitor - JSON Lines Format
- Amazon SageMaker Clarify Model Bias Monitor for Batch Transform - JSON Lines Format
- Amazon SageMaker Clarify Model Explainability Monitor for Batch Transform - JSON Lines Format