Which Azure service is used for operationalizing machine learning models?

Azure Machine Learning is a comprehensive service specifically designed to operationalize machine learning models effectively. It provides a range of tools and features that facilitate the entire lifecycle of machine learning workflows, from model development to deployment and monitoring.

The service allows data scientists to not only build and train models but also to deploy those models as web services, enabling real-time predictions. Azure Machine Learning offers capabilities such as automated machine learning, model versioning, and integration with CI/CD pipelines, which are crucial for operationalization. It also provides insights and analytics to help track model performance over time, ensuring that deployed models maintain their accuracy and effectiveness as new data becomes available.

While Azure Functions can help in creating serverless applications that can respond to events, it is not specifically targeted for machine learning operations. Azure Kubernetes Service is used for container orchestration, which can be beneficial for deploying models, but it requires additional setup for machine learning operations. Azure DevOps focuses on the collaboration and management of development projects, which includes CI/CD practices but lacks dedicated machine learning features compared to Azure Machine Learning.