IMPLEMENTING PAY-AS-YOU-GO (PAYG) AUTOMATED MACHINE LEARNING AND AI

摘要

A system and method for assessing Pay-As-You-Go (PAYG) Automatic machine learned (AutoML) model pipeline charge to a user on the basis of performance improvement achieved by configuring a model pipeline with performance enhancements relative to a performance obtained by a base model pipeline. The method performs a ranking of pipelines (customized models) based on a user-specified metric (for example, prediction accuracy, run time, F1 score) or combination of metrics. The price for ranked pipelines is specified based on a “surrogate” model where the surrogate model is fit to the base model price and the maximum price for a model. The base model price relates to use of a current cloud resource utilization-based pricing model. The pricing per model pipeline increments on the basis of performance metric(s) in a linear fashion, e.g., using a linear pricing model, or in an exponential fashion, e.g., using a fixed percentage hike price model.