Preparing the SageMaker Processing prerequisites using the AWS CLI
One of the most important steps in the machine learning process involves the preparation, processing, and transformation of the data before the actual training step. After the training step, the data needs to be analyzed and may need to be processed further before and during the evaluation step. Amazon SageMaker Processing is one of the most powerful options for fulfilling these types of requirements.
If you have a custom data processing script (for example, a data transformation script), your data is stored in an Amazon S3 bucket, or you are planning to run this script in an isolated managed environment that can easily be configured to handle larger datasets for production workloads at a later stage, then the next three recipes are for you!
Tip
Technically, you can use Amazon SageMaker Processing for any processing requirement that involves using a managed service to handle the infrastructure component and...
