Coordination-aware assurance for end-to-end machine learning systems: the R3E approach

Loading...
Thumbnail Image
Journal Title
Journal ISSN
Volume Title
A3 Kirjan tai muun kokoomateoksen osa
Date
2022
Major/Subject
Mcode
Degree programme
Language
en
Pages
339-367
Series
AI Assurance: Towards Trustworthy, Explainable, Safe, and Ethical AI
Abstract
Concerns of robustness, reliability, resilience, and elasticity in Machine Learning (ML) systems are important, and they must be considered in trade-off with efficiency factors. However, they need to be supported and optimized in an end-to-end manner, not just for ML models. In this chapter we present a conceptual approach to architectural design and engineering of the robustness, reliability, resilience, and elasticity (R3E) for end-to-end big data ML systems at runtime. We propose quality of analytics as a contractual means for optimizing end-to-end big data machine learning (BDML) systems. Based on that, we propose to define and abstract diverse types of components under R3E objects and devise operations and metrics for managing R3E attributes. Through a set of proposed coordination, monitoring, analytics, and testing methods, we identify essential tasks for tackling R3E concerns when developing BDML systems. Finally, we illustrate our approach with an example of an end-to-end BDML system for building objects classifications.
Description
Keywords
Other note
Citation
Truong , L 2022 , Coordination-aware assurance for end-to-end machine learning systems: the R3E approach . in F Batarseh & L Freeman (eds) , AI Assurance: Towards Trustworthy, Explainable, Safe, and Ethical AI . Elsevier , pp. 339-367 . https://doi.org/10.1016/B978-0-32-391919-7.00024-X