Build and operate machine learning solutions with Azure Machine Learning

Data Scientist
Machine Learning
Azure Portal

Azure Machine Learning is a cloud platform for training, deploying, managing, and monitoring machine learning models. Learn how to use the Azure Machine Learning Python SDK to create and manage enterprise-ready ML solutions.


This learning path assumes that you have experience of training machine learning models with Python and open-source frameworks like Scikit-Learn, PyTorch, and Tensorflow. If not, you should complete the Create machine learning models learning path before starting this one.

Modules in this learning path

Azure Machine Learning provides a cloud-based platform for training, deploying, and managing machine learning models.

Learn how to use Azure Machine Learning to train a model and register it in a workspace.

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.

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.

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.

Learn how to register and deploy ML models with the Azure Machine Learning service.

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.

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.

Learn how to use automated machine learning in Azure Machine Learning to find the best model for your data.

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.

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.

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.

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.

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.

Explore and experiment with securing a machine learning environment to ensure data remains private and models are accurate.