Machine Learning (ML) is a subset of artificial intelligence that enables systems to learn from data and improve over time without being explicitly programmed. Azure provides a suite of tools to develop, train, deploy, and manage ML models at scale.

• Supervised Learning – Uses labeled data to train a model (e.g., predicting housing prices).
• Unsupervised Learning – Finds patterns in unlabeled data (e.g., customer segmentation).
• Reinforcement Learning – Learns through trial and error in an environment (e.g., game AI).

1. Azure Machine Learning Studio

• A web-based platform for drag-and-drop model building.
• Great for beginners and non-programmers.

2. Azure ML SDK & CLI

• Tools for professional developers and data scientists.
• Allows full control over experiments, pipelines, and deployment.

3. Datasets and Compute Targets

• Easily manage datasets within Azure ML.
• Choose between local compute, Azure virtual machines, or GPU clusters.

4. Model Training & Evaluation

• Automate model selection with AutoML.
• Monitor accuracy, precision, recall, and other performance metrics.

5. Deployment

• Models can be deployed as REST APIs.
• Supports scaling, version control, and monitoring.

• Azure includes tools for bias detection, explainability, and model fairness.
• Integration with Responsible AI dashboard for audit and transparency.

You need to:

• Understand the ML lifecycle (data → train → evaluate → deploy).
• Know when to use Azure ML Studio vs. Cognitive Services.
• Identify the benefits of using Azure for end-to-end ML workflows.

• Cognitive Services Overview
• Azure ML Studio (in detail)