Isabelle Van Campenhoudt walks us through a scenario of using Azure ML to find expense reports which should automatically be approved, reducing the workload for approvers:
My partner in crime Serge Luca aka Doctor Flow is the author of a nice and complex expenses approval system in Microsoft Flow .
One year ago, he asked me to add analytics to his Flow. This year he has the interesting idea to add a machine-learning based approval in his flow and suggest me to work on it. The idea is the following: Since we have a lot of approvals in our system, can a machine learn and found some decision pattern to apply automatically to each expenses request ?
I decided to use the Microsoft Azure Machine Learning Studio. In this tool you can build experiments and use some of the most common and useful machine learning algorithms. It was amazing to see how easy it is to create and consume machine learning .
This contrasts with Ginger Grant’s nightmare scenario pretty well: instead of trying to get the ML process to do all of the work, create a process which takes care of the really easy stuff and leave harder tasks to specialists with a deeper understanding of the rules. That way they don’t have to spend their time on trivialities.