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A fintech company is developing an AI-driven fraud detection system using Amazon SageMaker. The system must provide end-to-end ML capabilities, including data preprocessing, model training, versioned model storage, deployment, and monitoring. Customer transaction data is stored securely in Amazon S3. The company requires the following: Secure access to training data for different ML workflows to ensure data isolation. A centralized model registry to manage model versions and deployments with minimal operational overhead. What is the most appropriate approach to meet these requirements with the LEAST operational overhead?