State-of-the-art machine learning (ML) systems show exceptional qualitative performance, but can also have a negative impact on society. With regard to global climate change, the question of resource consumption and sustainability becomes more and more urgent. The enormous energy footprint of single ML applications and experiments was recently investigated. However, environment-aware users require a unified framework to assess, compare, and report the efficiency and performance trade-off of different methods and models. In this work we propose novel efficiency aggregation, indexing, and rating procedures for ML applications. To this end, we devise a set of metrics that allow for a holistic view, taking both task type, abstract model, software, and hardware into account. As a result, ML systems become comparable even across different execution environments. Inspired by the EU’s energy label system, we also introduce a concept for visually communicating efficiency information to the public in a comprehensible way. We apply our methods to over 20 SOTA models on a range of hardware architectures, giving an overview of the modern ML efficiency landscape.