Technical requirementsGetting started with the SageMaker Python SDKPreparing the essential prerequisitesCreating a service limit increase requestTraining an image classification model with the SageMaker Python SDKCreating a new Notebook in SageMaker StudioDownloading the training, validation, and test datasetsUploading the data to S3Using the SageMaker Python SDK to train an ML modelUsing the %store magic to store dataUsing the SageMaker Python SDK to deploy an ML modelUsing the Debugger Insights DashboardUtilizing Managed Spot Training and CheckpointsCleaning upSummaryFurther reading