Designing and Implementing a Data Science Solution (Cloud Share)
Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.
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. Explore the Azure Machine Learning workspace |
60 mins
|
| 2. Explore developer tools for workspace interaction |
60 mins
|
| 3. Make data available in Azure Machine Learning |
60 mins
|
| 4. Work with compute resources in Azure Machine Learning |
60 mins
|
| 5. Work with environments in Azure Machine Learning |
60 mins
|
| 6. Train a model with the Azure Machine Learning Designer |
60 mins
|
| 7. Find the best classification model with Automated Machine Learning |
60 mins
|
| 8. Track model training in notebooks with MLflow |
60 mins
|
| 9. Run a training script as a command job in Azure Machine Learning |
60 mins
|
| 10. Use MLflow to track training jobs |
60 mins
|
| 11. Perform hyperparameter tuning with a sweep job |
10 mins
|
| 12. Run pipelines in Azure Machine Learning |
60 mins
|
| 13. Create and explore the Responsible AI dashboard |
60 mins
|
| 14. Log and register models with MLflow |
60 mins
|
| 15. Deploy a model to a batch endpoint |
60 mins
|
| 16. Deploy a model to a managed online endpoint |
60 mins
|
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