In recent years, AWS customers have been running machine learning (ML) on an increasing variety of datasets and data sources. Because a large percentage of organizational data is stored in relational databases such as Amazon Aurora, there’s a common need to make this relational data available for training ML models, and to use ML models to make predictions in database-based applications. This post shows how to easily extract your production data from Aurora, train an ML model in Amazon SageMaker, and integrate the model inferences back into your production database and applications. It extends a popular ML use case, predicting customer churn, and demonstrates how to achieve the real business goal of preventing customer churn. We’ll use a large phone company as our setting.
At our telco company, our CEO called us all into a meeting. “We have around 15% of customers leaving our service, or “churning”, every year! Losing

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