Trainieren und Verwalten eines Machine Learning-Modells mit Azure Machine Learning

Make data available in Azure Machine Learning

• Understand URIs
• Create a datastore
• Create a data asset

Work with compute targets in Azure Machine Learning

• Choose the appropriate compute target
• Create and use a compute instance
• Create and use a compute cluster

Work with environments in Azure Machine Learning

• Understand environments
• Explore and use curated environments
• Create and use custom environments

Run a training script as a command job in Azure Machine Learning

• Convert a notebook to a script
• Run a script as a command job
• Use parameters in a command job

Track model training with MLflow in jobs

• Track metrics with MLflow
• View metrics and evaluate models

Register an MLflow model in Azure Machine Learning

• Log models with MLflow
• Understand the MLflow model format
• Register an MLflow model

Deploy a model to a managed online endpoint

• Explore managed online endpoints
• Deploy your MLflow model to a managed online endpoint
• Deploy a model to a managed online endpoint
• Test managed online endpoints