Learning path 1: Design a machine learning solution
- Design a data ingestion solution for machine learning projects
- Design a machine learning model training solution
- Design a model deployment solution
- Design a machine learning operations (MLOps) solution
Learning Path 2: Explore and configure the Azure Machine Learning workspace
- Explore the Azure Machine Learning workspace resources and assets
- Explore developer tools for workspace interaction
- Make data available in Azure Machine Learning
- Work with compute resources in Azure Machine learning
- Work with environments in Azure Machine Learning
Learning Path 3: Experiment with Azure Machine Learning
- Explore Automated Machine Learning
- Find the best classification model with Automated Machine Learning
- Track model training in notebooks with MLflow
Learning Path 4: 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
Learning Path 5: Manage and evaluate models in Azure Machine Learning
- Register an MLflow model in Azure Machine Learning
- Create and explore the Responsible AI dashboard
Learning Path 6: Deploy and consume models with Azure Machine Learning
- Deploy a model to a managed online endpoint
- Deploy a model to a batch endpoint