Agenda

• Design a machine learning solution
• Design a data ingestion strategy for machine learning projects
• Design a machine learning model training solution
• Design a model deployment solution
• Design a machine learning operations solution
• Explore the Azure Machine Learning workspace
• Explore Azure Machine Learning workspace resources and assets
• Explore developer tools for workspace interaction
• Work with data in Azure Machine Learning
• Make data available in Azure Machine Learning
• Understand URIs
• Create a datastore
• Create a data asset
• Work with compute 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
• Use notebooks for experimentation in Azure Machine Learning
• Track model training in Jpyter notebooks with MLflow
• Train models with scripts in 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
• Optimize model training with pipelines in 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
• Deoploy a model to a managed online endpoint
• Deploy a model to a batch endpoint