Which Azure service can manage and monitor deployed machine learning models?

Azure Machine Learning Studio is the correct choice for managing and monitoring deployed machine learning models. This service offers a comprehensive platform specifically designed for machine learning workflows, which includes capabilities for experiment tracking, model registration, deployment, and performance monitoring.

Within Azure Machine Learning Studio, users can track model performance metrics, conduct A/B testing, and implement continuous integration/continuous delivery (CI/CD) practices for their models. This enables teams to ensure that their models perform well in production and can be retrained or updated as needed based on monitoring insights.

In contrast, while Azure DevOps can be utilized to manage the overall development lifecycle, including version control and project management, it does not specifically provide the functions tailored to monitor machine learning models directly. Azure Monitor is mainly focused on observing and analyzing the performance and health of Azure resources and applications but does not provide dedicated tools for managing machine learning models specifically. Azure Sentinel is a security information event management (SIEM) solution and is not related to machine learning model management or monitoring.

Thus, Azure Machine Learning Studio stands out as the solution tailored for the needs of deploying and monitoring ML models effectively.