The mission of the PASS Cloud Virtual Group is to enable cloud knowledge sharing and create a network of Cloud Computing professionals.

Introduction to the Azure Machine Learning Workbench with Ginger Grant

Tue, Jan 23 2018 18:00 UTC

Introduction to the Azure Machine Learning Workbench

Azure Machine Learning Workbench is a tool which Microsoft released into Public Preview at Ignite in September 2017. This tool contains a lot of really neat features, which Microsoft previously code named Project Pendleton, for cleaning and modifying your data even better than is possible in Power BI's Power Query. New to machine learning concepts? Azure Machine Learning Workbench has learning templates to help you get started. Have some python code lying around which you want to run the cloud to take advantage of infinite scaling? Azure Machine Learning Workbench can help you with that. Microsoft has some new direction for how Machine Learning can be called from within an Azure environment and this session will show how this application is part of that environment. Everything demonstrated in the session will cost nothing, so if you like what you see you can get started without paying out a dime. As this is a new product, this is one of the few places to find out all about it, so you won't want to miss this session.


Ginger Grant

Ginger Grant provides consulting services in advanced analytic solutions, including machine learning, data warehousing, data visualization and cube development using the entire Microsoft Data Stack, including SQL Server, T-SQL, SSIS, SSAS, Power BI and Azure. Ginger started working with data to solutions across a wide range of industries including insurance, education, healthcare, finance and transportation. She is a prolific blogger at http://www.desertislesql.com and frequent speaker and as a MCT provides training for training courses, at conferences and events worldwide to introduce more people to current developments and future trends in data. Microsoft has awarded her an MVP in Data Platform since 2016.

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