Blogs and whitepapers
The following blogs use a case study of sentiment prediction for a movie review to illustrate the process of executing a complete machine learning workflow. This includes data preparation, monitoring Spark jobs, and training and deploying a ML model to get predictions directly from your Studio or Studio Classic notebook.
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To extend the use case to a cross-account configuration where SageMaker Studio or Studio Classic and your Amazon EMR cluster are deployed in separate AWS accounts, see Create and manage Amazon EMR clusters from SageMaker Studio or Studio Classic to run interactive Spark and ML workloads - Part 2
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See also:
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A walkthrough of the configuration of Access Apache Livy using a Network Load Balancer on a Kerberos-enabled Amazon EMR cluster
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AWS whitepapers for SageMaker Studio or Studio Classic best practices.