Amazon Nova customization on SageMaker training jobs
Amazon SageMaker training jobs is an environment that enables you to train machine learning models at scale. It automatically provisions and scales compute resources, loads training data from sources like Amazon S3, executes your training code, and stores the resulting model artifacts.
The purpose of training is to customize the base Amazon Nova model using your proprietary data. The training process typically involves steps to prepare your data, choose a recipe, modify configuration parameters in YAML files, and submit a training job. The training process will output trained model checkpoint in a service-managed Amazon S3 bucket. You can use this checkpoint location for evaluation jobs. Nova customization on SageMaker training jobs stores model artifacts in a service-managed Amazon S3 bucket. Artifacts in the service-managed bucket are encrypted with SageMaker-managed KMS keys. Service-managed Amazon S3 buckets don't currently support data encryption using customer-managed KMS keys.
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