Azure Machine Learning

Get started with Azure ML concepts and platform overview.

Set up and configure an Azure ML workspace, understand its components, and manage compute resources effectively.

Learn how to import, clean, and preprocess datasets using Azure ML Studio and SDK for better model training.

Build machine learning models using Azure ML Designer, AutoML, and Jupyter Notebooks.

Train ML models at scale using Azure ML pipelines and optimize them with hyperparameter tuning techniques.

Deploy machine learning models as REST APIs or endpoints for real-time and batch inference.

Set up monitoring for deployed models, track performance, and manage model versions in production.

Implement CI/CD pipelines for machine learning workflows using Azure DevOps and GitHub Actions.

Combine Azure ML with Cognitive Services for advanced AI applications like vision, NLP, and speech recognition.

Ensure responsible AI deployment with proper data governance, security, and compliance on Azure ML.