# Blogs and whitepapers
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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.
+ [Create and manage Amazon EMR clusters from SageMaker Studio or Studio Classic to run interactive Spark and ML workloads](https://aws.amazon.com/blogs/machine-learning/part-1-create-and-manage-amazon-emr-clusters-from-sagemaker-studio-to-run-interactive-spark-and-ml-workloads/).
+ 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](https://aws.amazon.com/blogs/machine-learning/part-2-create-and-manage-amazon-emr-clusters-from-sagemaker-studio-to-run-interactive-spark-and-ml-workloads/).

See also: 
+ A walkthrough of the configuration of [Access Apache Livy using a Network Load Balancer on a Kerberos-enabled Amazon EMR cluster](https://aws.amazon.com/blogs/big-data/access-apache-livy-using-a-network-load-balancer-on-a-kerberos-enabled-amazon-emr-cluster/).
+ AWS whitepapers for [SageMaker Studio or Studio Classic best practices](https://docs.aws.amazon.com/whitepapers/latest/sagemaker-studio-admin-best-practices/sagemaker-studio-admin-best-practices.html).