Scoping Machine Learning Projects

Due to the torrent of investments and newcomers entering the Machine Learning (ML) playing field, we believe it is timely to share some of the insights that we have drawn from our experience in a broad range of roles and projects; particularly, the knowledge most relevant to scoping, selecting and implementing ML projects.

Most ML projects fail too late, after companies have invested enormous resources to preprocess data, write code and run computations to build prototypes. Hence, our goal with this guide is to help you to discard 80% of your ML project ideas already in the conceptualization phase.