Model Training Workflow in SageMaker – Automating the Learn Phase

Once your project is structured and AWS services are set up, the next step is to implement the training logic and automation. In this part, you’ll develop your training script, configure a SageMaker training job using the Python SDK, and ensure that outputs—like the trained model and logs—are stored in S3 for downstream steps. This is where your MLOps pipeline begins to “learn” from data in an automated, repeatable way.