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Key Features of AML Title: Core Features of Azure Machine Learning Content: Automated ML, drag-and-drop designer, model training, ML Ops, and integration with other Azure services.
AML Workspace Title: Understanding the AML Workspace Content: Overview of the workspace structure, including datasets, experiments, pipelines, and models.
Data Preparation Title: Data Preparation in AML Content: Tools and techniques for data cleaning, transformation, and feature engineering within AML.
Building and Training Models Title: Model Building and Training Content: Options for building models using Automated ML, the designer, or custom code (Python, R).
Model Deployment Title: Deploying Models with AML Content: Steps to deploy models as web services, A/B testing, and scaling in production.
ML Ops and CI/CD Title: Implementing ML Ops in AML Content: Continuous integration and delivery (CI/CD) for machine learning models using Azure DevOps.
Monitoring and Management Title: Monitoring and Managing Models Content: Tools for monitoring model performance, drift detection, and retraining in AML.
Use Cases and Applications Title: Real-World Applications of AML Content: Examples of how organizations use AML for predictive analytics, personalized recommendations, and more.
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