Which IBM Cloud service is used to run and deploy machine learning models anywhere across any cloud?

The choice of Watson Machine Learning is appropriate for running and deploying machine learning models across various environments and clouds. This service is specifically designed to create, train, and manage machine learning and deep learning models. One of its key features is the ability to deploy these models seamlessly on any cloud platform or within on-premises infrastructure, providing flexibility for organizations that operate in multi-cloud or hybrid environments.

Furthermore, Watson Machine Learning supports various frameworks and libraries, enabling data scientists and developers to utilize their preferred tools when building models. The service also includes capabilities for model monitoring and governance, which helps ensure that the deployed models perform effectively over time.

In contrast, other services like Watson Knowledge Services focus more on managing knowledge and insights rather than deploying machine learning models. Watson Open Scale is aimed at monitoring and managing AI models in production, while Watson Studio is primarily for data science and machine learning development, offering tools for building and training models but not specifically for their deployment across various infrastructures. Thus, Watson Machine Learning stands out as the most suitable option for model deployment across diverse cloud environments.