Article

Why Do Machine Learning Analytics Projects Fail?

Posted June 22, 2021 | Technology | Amplify
MLanalytics
Michael Papadopoulos and Philippe Monnot take a deep dive into ML projects. They address the “very powerful tendency to anthropomorphize ML and AI, imbuing it with human characteristics.” As we increasingly describe them in human terms, we often fail to make a critical distinction in the way humans and machines interpret the world.
About The Author
Michael Papadopoulos
Michael Papadopoulos is a Cutter Expert, Chief Architect of Arthur D. Little’s (ADL's) UK Digital Problem Solving practice, and a member of ADL's AMP open consulting network. He is passionate about designing the right solutions using smart-stitching approaches, even when elegance and architectural purity are overshadowed by practicality. Mr. Papadopoulos leads the scaling of multidisciplinary organizations by focusing on continuous improvement,… Read More
Philippe Monnot
Philippe Monnot is a Cutter Expert, a Data Scientist with Arthur D. Little's (ADL's) UK Digital Problem Solving practice, and a member of ADL's AMP open consulting network. He’s passionate about solving complex challenges that impact people’s livelihood through the use of data, statistics, and machine learning (ML). Mr. Monnot enjoys developing accessible solutions that customers will adopt through effective data storytelling and explainable… Read More
Don’t have a login? Make one! It’s free and gives you access to all Cutter research.