A healthcare company is building an AI application to predict patient readmission rates using Amazon SageMaker. The application must support end-to-end machine learning workflows, including data preprocessing, model training, version management, and deployment. The training data, stored securely in Amazon S3, must be used in isolated and secure environments to comply with regulatory requirements. As part of model experimentation, the data science team is running multiple training jobs back-to-back to test different hyperparameter configurations. To improve the team’s productivity, the company needs to reduce the startup time for each consecutive training job. What is the most efficient solution to achieve this goal?