In this repository, we demonstrate the power of automated machine learning (AutoML) by going into how FLAML - Microsoft's AutoML framework - can be used. We go over three examples in the following jupyter notebooks:
The first demo introduces the user to the concept of how FLAML can be used, by working on the commonly used IRIS dataset. Examples two and three highlight how FLAML, and more generally AutoML, can be used in an SAP context. The second notebook goes over a classification example, where FLAML uses purchase order data to determine if a purchase order will be accepted or rejected - this is the Purchase Order Requisition Use Case. The third notebook goes over a regression example, where FLAML uses item data to determine the max allocation (or stock) of an item in a store - this is the Retail Order Use Case.
These demos cover the basics for how to use the FLAML library. For those who are interested in learning more, we strongly recommend their repository, and specifically the automl.py file, for a deeper dive.
Python version >= 3.8. Specific Python packages, and their installation commands, are already contained in the notebook examples.
Python downloads can be found here.
No known issues.
Create an issue in this repository if you find a bug or have questions about the content.
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Copyright (c) 2024 SAP SE or an SAP affiliate company. All rights reserved. This project is licensed under the Apache Software License, version 2.0 except as noted otherwise in the LICENSE file.