Azure Machine Learning (Azure ML) is a cloud-based machine learning service provided by Microsoft Azure. It enables data scientists and developers to build, deploy, and manage machine learning models at scale.

Key features of Azure ML include:

1. Data Preparation: Azure ML provides tools for data ingestion, data cleaning, and data transformation. It supports various data formats and can handle large datasets.
2. Model Development: Azure ML supports popular programming languages and frameworks such as Python, R, and TensorFlow. It provides a range of tools and libraries for developing machine learning models, including automated machine learning (AutoML) capabilities that can automatically select and tune models.
3. Model Training: Azure ML allows you to train your machine learning models using various compute resources, including CPU and GPU clusters. It can scale up or down based on your training needs, and it supports distributed training for large-scale models.
4. Model Deployment: Once you have trained your model, Azure ML helps you deploy it as a web service or as an API endpoint. This allows you to integrate your machine learning models into applications, workflows, or other services.
5. Model Monitoring and Management: Azure ML provides tools for monitoring the performance of deployed models, including tracking metrics and logging. It also supports model versioning and management, making it easier to iterate and update models over time.
6. Integration with Azure Services: Azure ML integrates with other Azure services, such as Azure Databricks, Azure Data Factory, and Azure DevOps, enabling end-to-end machine learning workflows and seamless integration with existing Azure infrastructure.
7. MLOps Capabilities: Azure ML supports MLOps (Machine Learning Operations) practices, providing capabilities for continuous integration and deployment (CI/CD), model validation, and automation of machine learning workflows.

Overall, Azure ML offers a comprehensive set of tools and services for building and deploying machine learning models in the cloud, making it easier to develop intelligent applications and solutions.