When Machine Learning Solutions Are Not Possible!

There is a widespread belief among most of the practitioners that Machine Learning (ML) solutions always lead to business improvement. Although ML-based approaches have brought unique capabilities to the businesses, there are some circumstances under which relying on ML solutions might have a negative impact, or even it might not be possible at all. The main objective of this article is to discuss different use cases in which employing ML does not fully address the targeted business problem. This article presents five scenarios and later introduces possible solutions to consider better solutions for each scenario. The most straightforward reason not to use ML solutions is the inadequate quantity of data which hinders training accurate models.