The course is part of this learning path
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).
To get started, we need to create accounts for Machine Learning Experimentation and Model Management, and install ML Workbench. Note that Workbench is only supported on Windows 10, Windows Server 2016, macOS Sierra, and macOS High Sierra, so if you’re not running one of those operating systems, then you’re out of luck.
In the Azure portal, click the New button, then “Data + Analytics”, and then “Machine Learning Experimentation”. Fill in the account name. I’ll just call mine “Experiment”. Choose the subscription that you want billed.
For the resource group, you can either create a new one or choose an existing one. I’ll create a new one called “ml-resources”. Choose a location that’s close to you and your users.
Leave the number of seats at 2 because the first 2 seats are free. For the storage account, again, you can create a new one or use an existing one. I’ll create a new one called “guymlstorage”. Then fill in the name you want for the workspace. I’ll call mine “GuyWorkspace”.
“Create Model Management account” should be checked. Fill in an account name. I’ll call mine “ModelMgmt”. For the “Model Management pricing tier”, select “DEVTEST” if it’s available on your subscription. If not, then select “S1”. Finally, click the Create button.
Now, to install ML Workbench, you need to get the installer from this web page. In the github readme for this course, I’ve included separate links for Windows and Mac installation. I’m on a Mac, so I’ll go through that installation process, but Windows installation is similar.
Download the installer. Then run it. Double-click. Open. Continue. Install. This is normally quite a long install, but I’ve already installed it once, so the installation files are still cached. It normally takes between 15 minutes and half an hour. We’ll launch Workbench in the next lesson, so if you’re ready, go to the next video.
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).