What is the process for setting up a training job in Sagemaker?

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The general process for setting up a training job in Amazon Sagemaker is as follows:

1. Create or select a S3 bucket to store the input data and output artifacts of the training job.

2. Prepare the training data and upload it to the S3 bucket.

3. Create a training script in Python that defines the machine learning model, the hyperparameters, and the input and output channels. This script should account for the SageMaker-specific environment variables that are automatically passed to the training container.

4. Create an Amazon SageMaker Training Job configuration specifying, for example, the instance type to use or the algorithm to implement.

5. Launch the training job. This will start an Amazon SageMaker TensorFlow container, for example, based on the specified configuration and using the configured input and output data channels.

6. Monitor the job progress and the logs and metrics generated by the training container.

7. Once the job is completed, retrieve the training artifacts from the output S3 bucket or by downloading the output model from the SageMaker console.

8. If appropriate, deploy the trained model to an Amazon SageMaker Endpoint for inference.