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Introduction to Azure Machine Learning
course-steps 2 certification 1 lab-steps 1

Contents

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Introduction
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Conclusion
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Overview
DifficultyIntermediate
Duration1h 7m
Students178

Description

Course Description

Azure Machine Learning Workbench is a front-end for a variety of tools and services, including the Azure Machine Learning Experimentation and Model Management services.

Workbench is a relatively open toolkit. First, you can use almost any Python-based machine learning framework, such as Tensorflow or scikit-learn. Second, you can train and deploy your models either on-premises or on Azure.

Workbench also includes a great data-preparation module. It has a drag-and-drop interface that makes it easy to use, but its features are surprisingly sophisticated.

In this course, you will learn how Workbench interacts with the Experimentation and Model Management services, and then you will follow hands-on examples of preparing data, training a model, and deploying a trained model as a predictive web service.

Learning Objectives

  • Prepare data for use by an Azure Machine Learning Workbench experiment.
  • Train a machine learning model using Azure Machine Learning Workbench.
  • Deploy a model trained in Azure Machine Learning Workbench to make predictions.

Intended Audience

  • Anyone interested in Azure’s machine learning services

Prerequisites

Resources

The github repository for this course can be found here.  

Transcript

I hope you enjoyed learning about Azure Machine Learning Workbench. Let’s do a quick review of what you learned.

Workbench supports almost any Python-based machine learning framework. It also includes a powerful data preparation tool. Workbench requires the Azure Machine Learning Experimentation and Model Management services. The Experimentation service includes a hosted git repository for your projects, which keeps track of your training runs. The Model Management service provides deployment, hosting, versioning, management, and monitoring for your trained models.

A typical process for running an experiment is to import data into a data source, create a data preparation dataflow, and then run a Python script to train the model.

A training script will typically perform four tasks: load the data, train a model using a subset of the data, evaluate the model’s accuracy using a separate subset of the data, and save the trained model.

A confusion matrix shows where the model is having difficulty. A ROC curve shows the tradeoff between correctly classifying as many of the instances of a category as possible and not incorrectly classifying instances of other categories as this category.

When you deploy a trained model, you also need two other files: a schema file and a scoring script. The scoring script must contain an init function that loads the trained model and a run function that takes a new data record as input and returns the model’s prediction. To deploy a model locally, you must have Docker installed.

For your first deployment, you need to make sure the Microsoft ContainerRegistry is registered in your subscription, create the deployment environment, activate your Model Management account, configure the deployment environment, and create the predictive web service.

Now you know how to prepare data for use by an Azure Machine Learning Workbench experiment; train a machine learning model in ML Workbench; and deploy a model trained in Workbench to make predictions based on new data.

To learn more about Azure ML Workbench, you can read Microsoft’s documentation. Also watch for new machine learning courses on Cloud Academy, because we’re always publishing new courses.

If you have any questions or comments, please let me know in the Comments tab below this video or by emailing support@cloudacademy.com. Thanks and keep on learning!

About the Author

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Guy launched his first training website in 1995 and he's been helping people learn IT technologies ever since. He has been a sysadmin, instructor, sales engineer, IT manager, and entrepreneur. In his most recent venture, he founded and led a cloud-based training infrastructure company that provided virtual labs for some of the largest software vendors in the world. Guy’s passion is making complex technology easy to understand. His activities outside of work have included riding an elephant and skydiving (although not at the same time).