Train and manage a machine learning model with Azure Machine Learning
To train a machine learning model with Azure Machine Learning, you need to make data available and configure the necessary compute. After training your model and tracking model metrics with MLflow, you can decide to deploy your model to an online endpoint for real-time predictions. Throughout this learning path, you explore how to set up your Azure Machine Learning workspace, after which you train and manage a machine learning model.
Prerequisites
None
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Modules in this learning path
Learn about how to connect to data from the Azure Machine Learning workspace. You're introduced to datastores and data assets.
Learn how to work with compute targets in Azure Machine Learning. Compute targets allow you to run your machine learning workloads. Explore how and when you can use a compute instance or compute cluster.
Learn how to use environments in Azure Machine Learning to run scripts on any compute target.
Learn how to convert your code to a script and run it as a command job in Azure Machine Learning.
Learn how to track model training with MLflow in jobs when running scripts.
Learn how to log and register an MLflow model in Azure Machine Learning.
Learn how to deploy models to a managed online endpoint for real-time inferencing.