Azure Machine Learning Studio is a cloud-based platform that simplifies building, training, and deploying machine learning models. This guide provides a step-by-step overview to get started with its key features.

1. Set Up Your Workspace

• Create a Workspace: Log in to the Azure portal and create an Azure Machine Learning workspace. This serves as the central hub for managing experiments, models, and resources.

• Access the Studio: Navigate to the Azure Machine Learning Studio and select your workspace.

2. Add Compute Resources

• Provision Compute: Add compute instances or clusters (e.g., CPU/GPU) for running experiments or deploying models.

• Start Compute: Ensure your compute instance is running before proceeding with notebooks or scripts.

3. Connect and Prepare Data

• Connect Data Sources: Link to cloud-based datastores, local files, or public datasets.

• Prepare Data: Use tools to clean, transform, and split data into training and validation sets.

4. Train a Model