inference pipelines using clarify

sagemaker-clarify/clarify-explainability-inference-pipelines/README.md

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+## Credit risk prediction and explainability with Amazon SageMaker
+
+This example shows how to user SageMaker Clarify to run explainability jobs on a SageMaker hosted inference pipeline. 
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+Below is the architecture diagram used in the solution:
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+![alt text](clarify_inf_pipeline_arch.png)
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+
+The notebook performs the following steps:
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+1. Prepare raw training and test data
+2. Create a SageMaker Processing job which performs preprocessing on the raw training data and also produces an SKlearn model which is reused for deployment.
+3. Train an XGBoost model on the processed data using SageMaker's built-in XGBoost container
+4. Create a SageMaker Inference pipeline containing the SKlearn and XGBoost model in a series
+5. Perform inference by supplying raw test data
+6. Set up and run explainability job powered by SageMaker Clarify
+7. Use open source shap library to create summary and waterfall plots to understand the feature importance better
+8. Run bias analysis jobs
+9. Clean up
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+
+The attached notebook can be run in Amazon SageMaker Studio. 
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+