The correct choice is Amazon SageMaker Studio because it provides a unified, fully managed workspace where teams can build, train, tune, and deploy models without provisioning or maintaining the underlying infrastructure.

Studio is an integrated development environment that combines notebooks, experiment management, model building, training, debugging, and deployment into a single web based interface. It handles provisioning and scaling of compute and storage behind the scenes so the research team can focus on model development instead of server setup and infrastructure management.

Amazon SageMaker Ground Truth is focused on data labeling and creating high quality training datasets and it does not offer the end to end IDE for building training and deployment in one managed workspace.

Amazon SageMaker Autopilot automates model building and tuning through AutoML but it is not the comprehensive interactive development and deployment environment that Studio provides.

Amazon SageMaker JumpStart offers pretrained models and solution templates to accelerate starts yet it serves as an accelerator rather than the central managed IDE for the full model lifecycle.

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