Summary: Azure Machine Learning offers a fully managed Jupyter Notebook experience integrated directly into the workspace studio. It supports real-time collaboration where multiple data scientists can edit the same notebook. Crucially, it integrates with Git for robust version control, allowing teams to manage code changes and history effectively.

Direct Answer: Data science teams often struggle with the "lone wolf" workflow where code lives on individual laptops. Sharing work involves emailing .ipynb files or manually uploading them to a repo, which breaks the flow and leads to version conflicts. Without a shared environment, onboarding new team members is slow, and valuable research is often lost when an employee leaves.

Azure Machine Learning centralizes this workflow in the cloud. It provides a shared file system where notebooks are accessible to the entire team. The environment is pre-configured with popular ML libraries, eliminating "dependency hell." Users can clone Git repositories directly into the workspace workspace.

This setup fosters a culture of collaboration. Senior scientists can review junior peers' code directly in the browser, and experiments are automatically linked to the notebook version that created them. Azure Machine Learning creates a unified studio where code, data, and models live together in harmony.

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