What are 'compute instances' in Azure Machine Learning?

Compute instances in Azure Machine Learning serve as scalable computing resources specifically designed for training machine learning models and facilitating model development. They provide the necessary processing power to execute complex algorithms and handle large datasets efficiently. By leveraging compute instances, data scientists can develop, train, and test their models in a flexible environment that can be scaled based on their computational needs.

This approach allows for greater efficiency and cost-effectiveness, as users can allocate resources dynamically based on the workload. Compute instances can also be customized with different configurations and can support a variety of frameworks and tools commonly used in machine learning tasks.

Other choices involve elements that do not accurately represent the functionality of compute instances. Virtual machines do relate to computing resources but are more focused on storage management rather than training models. Tools for tracking model performance pertain to the monitoring phase after model deployment, and the mention of API gateways is more aligned with the deployment and operationalization of models rather than their training and development. Thus, the key focus of compute instances is precisely on providing the computational capacity necessary for model training and development within Azure Machine Learning.