Course Overview
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.
Course Content
- 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
Polish
Angielski
Niemiecki
Italian
Niemcy
Szwajcaria
Arabia Saudyjska
Qatar
Zjednoczone Emiraty Arabskie
Egipt