Data-pre-processing.ipynb
This notebook built in SageMaker takes care of all the pre-processing activities like Data analysis, Merging datasets, Missing values, Feature engineering, Usecases formation & more. The updated csv files are uploaded onto S3 bucket programmatically.
RI-SageMaker-Deploy-Wstudio.ipynb
This notebooks builds a SageMaker Linear Learner predictive model for Risk Index prediction and deploys an endpoint on SageMaker platform.
Drift-Detection-Model.ipynb
This notebook sets up the Drift metrics in the Watson OpenScale dashboard to monitor the SageMaker endpoint for Drift in data and model performance.
SageMaker-Monitor-OpenScale.ipynb
This notebook sets up IBM DB2 & the Watson OpenScale dashboard programmatically for monitoring and configuring metrics like Fairness & Bias in the SageMaker endpoint.
Optional :- The below notebooks can be explored offline.
Risk_Index_Prediction.ipynb
This notebook built using SageMaker Linear Learner (in-built module) takes care of building multi-class classification ML model for prediction risk index per region. Upload the Notebook into Cloud Pak for Data environment using Watson Studio in the next step. Try uploading this notebook offline using the instructions.
WS-Flanders-Predict.ipynb & WS-Belgium-Predict.ipynb
These notebooks built in SageMaker using Deep Neural Networks takes care of building time-series forecasting models at the Region & Country levels. Upload the Notebooks into Cloud Pak for Data environment using Watson Studio in the next step. Try uploading these notebooks offline using the instructions.