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

Machine learning technologies have demonstrated immense capabilities invarious domains. They play a key role in the success of modern businesses.However, adoption of machine learning technologies has a lot of untouchedpotential. Cost of developing custom machine learning solutions that solveunique business problems is a major inhibitor to far-reaching adoption ofmachine learning technologies. We recognize that the monolithic natureprevalent in today's machine learning applications stands in the way ofefficient and cost effective customized machine learning solution development.In this work we explore the benefits of modular machine learning solutions anddiscuss how modular machine learning solutions can overcome some of the majorsolution engineering limitations of monolithic machine learning solutions. Weanalyze the trade-offs between modular and monolithic machine learningsolutions through three deep learning problems; one text based and the twoimage based. Our experimental results show that modular machine learningsolutions have a promising potential to reap the solution engineeringadvantages of modularity while gaining performance and data advantages in a waythe monolithic machine learning solutions do not permit.

Towards Modular Machine Learning Solution Development: Benefits and Trade-offs