1. Introduction to the Azure Machine Learning SDK
Azure Machine Learning provides a cloud-based platform for training, deploying, and managing machine learning models.
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2. Use Automated Machine Learning in Azure Machine Learning
Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier.
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3. Create a classification model with Azure Machine Learning designer
Classification is a supervised machine learning technique used to predict categories or classes. Learn how to create classification models using Azure Machine Learning designer.
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4. Train a machine learning model with Azure Machine Learning
Learn how to use Azure Machine Learning to train a model and register it in a workspace.
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5. Work with Data in Azure Machine Learning
Data is the foundation of machine learning. In this module, you will learn how to work with datastores and datasets in Azure Machine Learning, enabling you to build scalable, cloud-based model training solutions.
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6. Work with Compute in Azure Machine Learning
One of the key benefits of the cloud is the ability to use scalable, on-demand compute resources for cost-effective processing of large data. In this module, you'll learn how to use cloud compute in Azure Machine Learning to run training experiments at scale.
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7. Orchestrate machine learning with pipelines
Orchestrating machine learning training with pipelines is a key element of DevOps for machine learning. In this module, you'll learn how to create, publish, and run pipelines to train models in Azure Machine Learning.
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8. Deploy real-time machine learning services with Azure Machine Learning
Learn how to register and deploy ML models with the Azure Machine Learning service.
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9. Deploy batch inference pipelines with Azure Machine Learning
Machine learning models are often used to generate predictions from large numbers of observations in a batch process. To accomplish this, you can use Azure Machine Learning to publish a batch inference pipeline.
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10. Tune hyperparameters with Azure Machine Learning
Choosing optimal hyperparameter values for model training can be difficult, and usually involved a great deal of trial and error. With Azure Machine Learning, you can leverage cloud-scale experiments to tune hyperparameters.
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11. Automate machine learning model selection with Azure Machine Learning
Learn how to use automated machine learning in Azure Machine Learning to find the best model for your data.
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12. Explore differential privacy
Data scientists have an ethical (and often legal) responsibility to protect sensitive data. Differential privacy is a leading edge approach that enables useful analysis while protecting individually identifiable data values.
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13. Explain machine learning models with Azure Machine Learning
Many decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions models make.
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14. Detect and mitigate unfairness in models with Azure Machine Learning
Machine learning models can often encapsulate unintentional bias that results in unfairness. With Fairlearn and Azure Machine Learning, you can detect and mitigate unfairness in your models.
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15. Monitor data drift with Azure Machine Learning
Changing trends in data over time can reduce the accuracy of the predictions made by a model. Monitoring for this data drift is an important way to ensure your model continues to predict accurately.
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16. Monitor models with Azure Machine Learning
After a machine learning model has been deployed into production, it's important to understand how it is being used by capturing and viewing telemetry.
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