====== Azure Machine Learning Studio ======

Azure Machine Learning Studio is a cloud-based integrated development environment (IDE) that enables users to build, train, and deploy machine learning models with ease. It is designed for both beginners and experienced data scientists, offering both a drag-and-drop interface and programmatic capabilities.

===== 🧰 Key Features of Azure ML Studio =====

**1. Drag-and-Drop Interface**  
  * Allows users to build machine learning models visually, without coding.
  * Pre-built modules for data preparation, feature selection, model training, and evaluation.

**2. Automated Machine Learning (AutoML)**  
  * AutoML helps automatically select the best model and hyperparameters for a given dataset.
  * Ideal for users who want to leverage machine learning without deep expertise.

**3. Model Training and Evaluation**  
  * Supports popular algorithms like regression, classification, clustering, and deep learning.
  * Includes tools to evaluate model performance using metrics such as accuracy, precision, recall, and F1 score.

**4. Real-Time Scoring and Deployment**  
  * Deploy trained models as REST APIs directly from the studio.
  * Azure automatically handles scaling and deployment to Azure Kubernetes Service (AKS) or Azure Container Instances (ACI).

**5. Integration with Python and R**  
  * You can bring in custom code through Python and R to extend the capabilities of the studio.
  * This is useful for users who need custom models or specialized analysis beyond the drag-and-drop interface.

**6. Collaboration and Version Control**  
  * Azure ML Studio supports team collaboration and version control.
  * You can track experiments, share datasets, and manage different versions of your models.

===== 🧑‍💻 Workflow in Azure ML Studio =====

1. **Data Ingestion** – Import datasets from multiple sources like Azure Blob Storage or CSV files.
2. **Data Preprocessing** – Clean and transform data using built-in tools like data imputation, normalization, and encoding.
3. **Model Training** – Select and train models using pre-configured algorithms or custom code.
4. **Model Evaluation** – Use built-in evaluation metrics to assess model performance.
5. **Deployment** – Publish models as APIs and integrate with other applications.

===== 🧠 In the AI-900 Exam =====

You should be able to:

  * Understand the core features of Azure ML Studio.
  * Identify when to use Azure ML Studio vs. other Azure machine learning services.
  * Recognize the benefits of using AutoML for model selection.

===== 🔗 Related Topics =====

  * [[AI:AI900:azure_services:machine_learning|Machine Learning in Azure]]
  * [[AI:AI900:azure_services_*