ACE: Easy Deployment of Field Optimization Experiments

Mattos, David Issa; Bosch, Jan; Olsson, Helena Holmström

doi:10.1007/978-3-030-29983-5_18

Cited by 2 publications

(1 citation statement)

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“…When more than one split-test variation is being tested against the same control variant it is called A/B/n tests. Data from these experiments are collected and based on this the best treatment/variant can be selected [14]. Many companies perform A/B experiments to explore parameters and test these to find parameters that increases system performance [5,15].…”

Section: A/b/n Experimentsmentioning

confidence: 99%

Autonomously Improving Systems in Industry: A Systematic Literature Review

Green

Bosch

Olsson

2021

Lecture Notes in Business Information Processing

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A significant amount of research effort is put into studying machine learning (ML) and deep learning (DL) technologies. Real-world ML applications help companies to improve products and automate tasks such as classification, image recognition and automation. However, a traditional "fixed" approach where the system is frozen before deployment leads to a sub-optimal system performance. Systems autonomously experimenting with and improving their own behavior and performance could improve business outcomes but we need to know how this could actually work in practice. While there is some research on autonomously improving systems, the focus on the concepts and theoretical algorithms. However, less research is focused on empirical industry validation of the proposed theory. Empirical validations are usually done through simulations or by using synthetic or manually alteration of datasets. The contribution of this paper is twofold. First, we conduct a systematic literature review in which we focus on papers describing industrial deployments of autonomously improving systems and their real-world applications. Secondly, we identify open research questions and derive a model that classifies the level of autonomy based on our findings in the literature review.

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Section: A/b/n Experimentsmentioning

confidence: 99%