Which tool is preferred by data scientists for working with Azure Machine Learning?

The Azure Machine Learning Python SDK is preferred by data scientists because it provides a comprehensive and user-friendly interface for building, training, and deploying machine learning models on Azure. This SDK integrates seamlessly with Python, which is one of the most popular programming languages in the data science community, allowing data scientists to leverage existing Python libraries such as NumPy, Pandas, and Scikit-learn within their workflows.

Using the Python SDK, data scientists can perform essential tasks such as defining and managing datasets, creating experiments, tuning hyperparameters, and deploying models—all with efficient and concise code. This flexibility and ease of use make the SDK a powerful tool for end-to-end machine learning development on Azure.

Other options, while useful in their own rights, do not provide the same level of integration and functionality. The Azure Machine Learning CLI extension is useful for users who prefer command-line interfaces but lacks the richness of capabilities offered by the Python SDK. AmlCompute is a compute target used for running experiments rather than a direct tool for model development and management. MLClient refers more to a client interface for accessing Azure ML resources, which does not encompass the full range of functions provided by the Python SDK necessary for comprehensive data science tasks.