An ecommerce team trains a large image classification model with 10,000 classes across many training iterations. They need to minimize operational overhead and cost while avoiding loss of work and retraining. Which approach best satisfies these needs?

SageMaker managed spot training with checkpointing is the most effective solution. Managed spot training automatically handles Spot Instance interruptions by pausing the training job, saving its state (checkpoints) to S3, and resuming it on a new Spot Instance when available, minimizing cost and operational overhead while preventing data loss. Running AWS Batch jobs on Spot Instances requires manual setup for checkpointing and resumption, increasing operational overhead. Using raw EC2 Spot Instances with interruption notices requires custom code for saving and resuming, which is complex and prone to errors. AWS Lambda is unsuitable for long-running, compute-intensive tasks like training large image classification models due to its execution duration limits.