How deeply does Machine Learning System Design cover Wasserstein distance?

Defines transport-based distribution comparison and applies thresholding, but does not derive the metric or explain its algorithm.

Wasserstein distance

As explained in Machine Learning System Design

Wasserstein distance is a metric between probability distributions defined by the minimum cost of transporting mass from one distribution to another.

Wasserstein distance measures how much probability mass must move, and at what cost, to transform one distribution into another. In monitoring, it acts as an alert for prediction drift and data drift rather than as a standalone model-quality measure. section 14.5.1

Retail drift monitoring

A retail system tests Wasserstein distance above 0.1 as a drift alert alongside Population Stability Index above 0.2. When comparing April 2021 with April 2022, it scales the control data by the expected 15% business growth before computing the distance. The same approach supports early warnings for next-day, next-week, and next-month predictions. section 14.5.1

How Machine Learning System Design covers it

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Also explained in

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Learn Generative AI with PyTorchA distance measure used in Wasserstein GAN training to compare distributions and provide a smoother optimization signal than standard GAN losses.
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Machine Learning Platform EngineeringWasserstein distance is a measure of dissimilarity between probability distributions based on the minimum cost of transforming one distribution into another.
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Evolutionary Deep Learning: Genetic algorithms and neural networksA distance between probability distributions that measures the minimum transport cost required to transform one distribution into another.

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Related concepts

Wasserstein GANused by · A Wasserstein GAN is a generative adversarial network that uses a critic and a Wasserstein-based objective to compare generated and real-data distributions.
WGAN-GPused by · WGAN-GP is a Wasserstein generative adversarial network variant that adds a gradient penalty to constrain the critic and stabilize training.
population stability indexalternative to · The population stability index (PSI) is a measure of the difference between two probability distributions, often used to quantify shifts in a population or model inputs.
probability distributionmeasures · A probability distribution is a mathematical specification assigning probabilities to the possible values or outcomes of a random variable, with total probability one.
driftmeasures · Drift is a change over time in a system’s behavior, state, data, or configuration relative to a reference or expected baseline.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning System Design. The section links point to the parts of the book it draws on.