Amazon SageMaker
Developer Guide

Step 1.2: Create an S3 Bucket

In exercises where you create a model training job, you save the following in an Amazon S3 bucket:

  • The model training data

  • Model artifacts, which Amazon SageMaker generates during model training

You can store the training data and artifacts in a single bucket or in two separate buckets. For exercises in this guide, one bucket is sufficient. You can use existing buckets or create new ones.

Follow the instructions in Create a Bucket in the Amazon Simple Storage Service Console User Guide. Include sagemaker in the bucket name; for example, sagemaker-datetime.

Note

Amazon SageMaker needs permission to access this bucket. You grant permission with an IAM role, which you create in the next step (as part of creating an Amazon SageMaker notebook instance). This IAM role automatically gets permissions to access any bucket with sagemaker in the name through the AmazonSageMakerFullAccess policy that Amazon SageMaker attaches to the role.

Next Step

Step 2: Create an Amazon SageMaker Notebook Instance

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