English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

With most technical fields, there exists a delay between fundamental academicresearch and practical industrial uptake. Whilst some sciences have robust andwell-established processes for commercialisation, such as the pharmaceuticalpractice of regimented drug trials, other fields face transitory periods inwhich fundamental academic advancements diffuse gradually into the space ofcommerce and industry. For the still relatively young field ofAutomated/Autonomous Machine Learning (AutoML/AutonoML), that transitory periodis under way, spurred on by a burgeoning interest from broader society. Yet, todate, little research has been undertaken to assess the current state of thisdissemination and its uptake. Thus, this review makes two primary contributionsto knowledge around this topic. Firstly, it provides the most up-to-date andcomprehensive survey of existing AutoML tools, both open-source and commercial.Secondly, it motivates and outlines a framework for assessing whether an AutoMLsolution designed for real-world application is 'performant'; this frameworkextends beyond the limitations of typical academic criteria, considering avariety of stakeholder needs and the human-computer interactions required toservice them. Thus, additionally supported by an extensive assessment andcomparison of academic and commercial case-studies, this review evaluatesmainstream engagement with AutoML in the early 2020s, identifying obstacles andopportunities for accelerating future uptake.

The Technological Emergence of AutoML: A Survey of Performant Software and Applications in the Context of Industry