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
English
German
Polish
Italian
Germany
Switzerland
Qatar
Saudi Arabia
United Arab Emirates
Egypt
Including Microsoft Applied Skills Credential
Microsoft Applied Skills are scenario-based credentials that provide learners with validation of targeted skills. These credentials are an efficient and trusted way to identify and deepen proficiency in scenario-based skillsets. Applied Skills Credentials are assessed via an interactive lab environment at the end of the course.
Learn more about Microsoft Applied Skills