Train and deploy a machine learning model with Azure Machine Learning (Cloud Share)
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.
What's Included:
- 10 launches per module
- Free Access to production Azure Subscription (No sign up / credit cards)
- Free 24/7 premier support
- Free instructor access
Platform Features:
- Seamless paste to virtual machine functionality
- Integrated keyboard language translation for all countries
- Integrated step-by-step instructions
- Remote classroom management for instructors & admins
- Student progress reporting
- MS Learn content integration
| Lab | Estimated completion time |
|---|---|
| 1. Make data available in Azure Machine Learning |
60 mins
|
| 2. Work with compute targets in Azure Machine Learning |
60 mins
|
| 3. Run a training script as a command job in Azure Machine Learning |
60 mins
|
| 4. Track model training with MLflow in jobs |
60 mins
|
| 5. Log and register models with MLflow |
60 mins
|
| 6. Deploy a model to a managed online endpoint |
60 mins
|
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