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 deploy a machine learning model.
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Make data available in Azure Machine Learning
Work with compute targets in Azure Machine Learning
Work with environments in Azure Machine Learning
Run a training script as a command job in Azure Machine Learning
Track model training with MLflow in jobs
Register an MLflow model in Azure Machine Learning
Deploy a model to a managed online endpoint
SCARICA MODULO ISCRIZIONE
