AWS Advanced Analytics Jumpstarter

Collection of labs designed to enable users to perform advanced analytics on AWS

author: [email protected]

Quick Launch for base lab environment (us-west-2)

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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.

1. Workshop Presentation
2. Lab Guide.
3. 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.