Collection of labs designed to enable users to perform advanced analytics on AWS
author: dylatong@amazon.com
I. SageMaker Predictive Analytics (est. 1.5 hours)
Below is content you can package up to demonstrate how to run an Advanced Analytics project on SageMaker.
- Workshop Presentation
- Lab Guide.
- Lab: Predictive Churn Analytics:
- Learn how to query ground truth data from our data warehouse into a pandas dataframe for exploration and feature engineering.
- Train an XGBoost model to perform churn prediction.
- Learn how to run a Batch Transform job to calculate churn scores in batch.
- Optimize your model using SageMaker Neo.
- Run an AWS Glue job programatically to demonstrate data processing and feature engineering at scale using SparkML.
- Create a production scale inference pipeline that consists of a SparkML feature engineering pipeline that feeds into an XGBoost churn classification model.