Using Azure ML Workbench
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.
- 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.
- Anyone interested in Azure’s machine learning services
- Introduction to Azure Machine Learning Studio course or basic machine learning experience.
- Python experience.
- Azure account recommended (sign up for free trial here if you don’t already have an account).
Welcome to “Introduction to Azure Machine Learning Workbench”. My name’s Guy Hummel and I’ll be showing you how to build machine learning models with Microsoft’s new, more flexible, toolkit. I’m a Research Lead at Cloud Academy and I have over 10 years of experience with cloud technologies. If you have any questions, feel free to connect with me on LinkedIn and send me a message, or send an email to email@example.com.
This course is intended for anyone who’s interested in Azure’s machine learning services.
To get the most from this course, you should have some experience with basic machine learning. If you don’t, then you can take my “Introduction to Azure Machine Learning Studio” to learn the fundamentals. You should also have some experience with the Python programming language. The best way to learn is by doing, so I recommend that you try performing these tasks yourself on your own Azure account. If you don’t already have one, then you can create a free trial account.
To save you the trouble of typing in the URLs and commands shown in this course, I’ve put them in a readme file in a github repository. You can find a link to the repository at the bottom of the “About this course” tab below this video.
First, I’ll give you an overview of Machine Learning Workbench and its related Azure services. Then we’re going to install ML Workbench and set up its required Azure dependencies. Next, we’ll go through an example of how to do basic data preparation in Workbench. Then, we’ll use the data we prepared and train a machine learning model with it. After that, we’ll deploy that model as a predictive web service. And finally, we’ll go through an example of doing more advanced data preparation.
By the end of this course, you should be able 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.
We’d love to get your feedback on this course, so please let us know what you think on the Comments tab below or by emailing firstname.lastname@example.org.
Now, if you’re ready to learn how to get the most out of Azure Machine Learning Workbench, then let’s get started.
About the Author
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).