Azure Machine Learning is a cloud platform for training, deploying, managing, and monitoring machine learning models. In this course, you will learn how to use the Azure Machine Learning Python SDK to create and manage enterprise-ready ML solutions.
Build and Operate Machine Learning Solutions with Azure
This course is part of Microsoft Azure Data Scientist Associate (DP-100) Exam Prep Professional Certificate
Instructor: Microsoft
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What you'll learn
Learn how to use the Azure Machine Learning Python SDK to create and manage enterprise-ready ML solutions
Work with Data and Computer in Azure Machine Learning
Use the Azure Machine Learning SDK to train a model. Select models and protect sensitive data
Orchestrate pipelines and deploy real-time machine learning services with Azure Machine Learning
Skills you'll gain
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12 quizzes, 23 assignments
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There are 6 modules in this course
Azure Machine Learning provides a cloud-based platform for training, deploying, and managing machine learning models. In this module, you will learn how to provision an Azure Machine Learning workspace. You will use tools and interfaces to work with Azure Machine Learning and run code-based experiments in an Azure Machine Learning workspace. finally, you will learn how to use Azure Machine Learning to train a model and register it in a workspace.
What's included
8 videos9 readings4 quizzes2 assignments1 discussion prompt
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. You'll also learn how to use cloud compute in Azure Machine Learning to run training experiments at scale.
What's included
8 videos9 readings1 quiz4 assignments
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. You'll also learn how to register and deploy ML models with the Azure Machine Learning service.
What's included
7 videos10 readings3 quizzes2 assignments
Machine learning models are often used to generate predictions from large numbers of observations in a batch process. You will accomplish this using Azure Machine Learning to publish a batch inference pipeline. You will also leverage cloud-scale experiments to choose optimal hyperparameter values for model training.
What's included
6 videos6 readings4 quizzes1 assignment
In this module, you will learn how to use automated machine learning in Azure Machine Learning to find the best model for your data. You will learn how differential privacy is a leading edge approach that enables useful analysis while protecting individually identifiable data values. You will also learn about the factors that influence the predictions models make.
What's included
13 videos8 readings7 assignments
Machine learning models can often encapsulate unintentional bias that results in unfairness. In this module, you will learn how to use Fairlearn and Azure Machine Learning to detect and mitigate unfairness in your models. You will learn how to use telemetry to understand how a machine learning model is being used once it has been deployed into production. Finally, you will learn how to monitor data drift to ensure your model continues to predict accurately.
What's included
13 videos8 readings7 assignments1 discussion prompt
Recommended if you're interested in Machine Learning
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