A new and faster machine learning flywheel for enterprises

This post is a commentary on the MLCommons article "Perspective: Unlocking ML requires an ecosystem approach" by Peter Mattson, Aarush Selvan, David Kanter, Vijay Janapa Reddi, Roger Roberts, and Jacomo Corbo. The world of artificial intelligence (AI) and machine learning (ML) is undergoing a sea change from science to engineering at scale. Over the past decade, the volume of AI research has skyrocketed as the cost to train and deploy commercial models has decreased. Between 2015 and 2021, the cost to train an image classification system fell by 64 percent, while training times improved by 94 percent in the same period.1 The emergence of foundation models--large-scale, deep learning models trained on massive, broad, unstructured data sets--has enabled entrepreneurs and business executives to see the possibility of true scale.