Customers in many different domains tend to work with multiple sources for their data: object-based storage like Amazon Simple Storage Service (Amazon S3), relational databases like Amazon Relational Database Service (Amazon RDS), or data warehouses like Amazon Redshift. Machine learning (ML) practitioners are often driven to work with objects and files instead of databases and tables from the different frameworks they work with. They also prefer local copies of such files in order to reduce the latency of accessing them.
Nevertheless, ML engineers and data scientists might be required to directly extract data from data warehouses with SQL-like queries to obtain the datasets that they can use for training their models.
In this post, we use the Amazon SageMaker Processing API to run a query against an Amazon Redshift cluster, create CSV files, and perform distributed processing. As an extra step, we also train a simple model to predict the

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