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).
Why did Microsoft develop Azure Machine Learning Workbench? After all, Azure Machine Learning Studio has been around for years and it’s very easy to use. In contrast, Workbench is not as easy to use and requires you to write code and use the command line. So what are the advantages of using Workbench? Well, in a word, it’s more open. ML Studio is great, but it’s a closed system. You have no control over what machine learning framework you can use, where your models get trained, or where they get deployed.
Workbench is pretty much the opposite. It’s essentially a front-end for a variety of tools and services. You can use almost any Python-based machine learning framework, such as Tensorflow, Microsoft Cognitive Toolkit, or PyTorch. You can train your models on-premises or on Azure. Similarly, you can deploy a model locally, or on a Data Science Virtual Machine, or on an Azure Container cluster.
Workbench is integrated with two Azure Machine Learning services: the Experimentation service and the Model Management service. The Experimentation service includes a hosted git repository for your projects, which keeps track of your training runs. This makes it easy to go back and review the inputs, outputs, and metrics of your experiments, so you can understand how your changes have affected your results. The Experimentation service comes with a command-line interface, but ML Workbench provides a graphical front-end to it, to make it easier.
The Model Management service provides deployment, hosting, versioning, management, and monitoring for your trained models. At the moment, this isn’t fully integrated with Workbench, so you still have to use the command line quite a bit.
One of the best features of Workbench is its data preparation module. It provides a drag-and-drop interface for data wrangling. It’s quite sophisticated and easy to use, as you’ll see, because we’ll be spending a significant amount of time using it to prepare data in this course.
I should mention that all three of these products are still in beta at the moment, so some of the features may change rapidly.
That’s it for the overview, so if you’re ready, let’s move on to installing Workbench.
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).