Workspace: Azure Machine Learning Studio provides a workspace where you can organize and manage your machine learning assets, such as datasets, experiments, and models.
Data Preparation: You can use the platform to explore and prepare your data for machine learning tasks. This includes importing data, cleaning it, and transforming it into the necessary format.
Experimentation: Azure Machine Learning Studio supports the creation and execution of machine learning experiments. You can build, train, and evaluate models using various algorithms and techniques.
Model Deployment: Once you've trained a model, you can deploy it as a web service on Azure, making it accessible for predictions.
Integration with Other Azure Services: Azure Machine Learning Studio is designed to integrate seamlessly with other Azure services, allowing you to take advantage of features like Azure Databricks, Azure Notebooks, and more.
Collaboration and Versioning: The platform supports collaboration among team members, and it provides version control for your machine learning assets.
Automated Machine Learning (AutoML): Azure Machine Learning Studio includes AutoML capabilities, allowing users to automate the process of selecting and tuning machine learning models.
