Conclusion
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
- 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).
Resources
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).