Diving deep on SageMaker Autopilot
In this section, we're going to learn in detail how SageMaker Autopilot processes data and trains models. If this feels too advanced for now, you're welcome to skip this material. You can always revisit it later once you've gained more experience with the service.
First, let's look at the artifacts that SageMaker Autopilot produces.
The job artifacts
$ aws s3 ls s3://sagemaker-us-east-2-123456789012/sagemaker/DEMO-autopilot/output/my-first-autopilot-job/
We can see many new prefixes. Let's figure out what's what:
PRE data-processor-models/PRE preprocessed-data/PRE sagemaker-automl-candidates/PRE transformed-data/PRE tuning/
preprocessed-data/tuning_data prefix contains the training and validation splits generated from the input dataset. Each split is further broken into small CSV chunks: