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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 Outline
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