Explore and configure the Azure Machine Learning workspace
Explore Azure Machine Learning workspace resources and assets
Explore developer tools for workspace interaction
Make data available in Azure Machine Learning
Work with compute targets in Azure Machine Learning
Work with environments in Azure Machine Learning
Experiment with Azure Machine Learning
Find the best classification model with Automated Machine Learning
Track model training in Jupyter notebooks with MLflow
Optimize model training with Azure Machine Learning
Run a training script as a command job in Azure Machine Learning
Track model training with MLflow in jobs
Perform hyperparameter tuning with Azure Machine Learning
Run pipelines in Azure Machine Learning
Manage and review models in Azure Machine Learning
Register an MLflow model in Azure Machine Learning
Create and explore the Responsible AI dashboard for a model in Azure Machine Learning
Deploy and consume models with Azure Machine Learning
Deploy a model to a managed online endpoint
Deploy a model to a batch endpoint
Develop generative AI apps in Azure AI Foundry portal
Plan and prepare to develop AI solutions on Azure
Explore and deploy models from the model catalog in Azure AI Foundry portal
Develop an AI app with the Azure AI Foundry SDK
Get started with prompt flow to develop language model apps in the Azure AI Foundry
Build a RAG-based agent with your own data using Azure AI Foundry
Fine-tune a language model with Azure AI Foundry
Evaluate the performance of generative AI apps with Azure AI Foundry
Responsible generative AI