Which service in Azure is primarily used for building and deploying machine learning models?

Azure Machine Learning is designed specifically for the development and deployment of machine learning models. This service provides a comprehensive environment that caters to various stages of the machine learning lifecycle, including data preparation, model training, deployment, and management.

With Azure Machine Learning, data scientists and developers can leverage features like automated machine learning, feature engineering, and integrations with popular machine learning frameworks such as TensorFlow, PyTorch, and Scikit-Learn. The platform also offers tools for version control, monitoring, and scaling which are essential for deploying models effectively in production environments.

In contrast, Azure Functions is primarily a serverless computing service that allows users to run small pieces of code without managing infrastructure, not specifically tailored for machine learning tasks. Azure Blob Storage is used for storing large amounts of unstructured data, such as images or logs, and while it can be a component of a machine learning project (for data storage), it does not provide the tools necessary for model development or deployment. Azure Logic Apps focuses on automating workflows and integrating applications and services, which also falls outside the scope of direct machine learning model handling.

Overall, Azure Machine Learning stands out as the dedicated service for end-to-end machine learning processes, making it the best answer for the question.