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

As big data grows ubiquitous across many domains, more and more stakeholdersseek to develop Machine Learning (ML) applications on their data. The successof an ML application usually depends on the close collaboration of ML expertsand domain experts. However, the shortage of ML engineers remains a fundamentalproblem. Low-code Machine learning tools/platforms (aka, AutoML) aim todemocratize ML development to domain experts by automating many repetitivetasks in the ML pipeline. This research presents an empirical study of around14k posts (questions + accepted answers) from Stack Overflow (SO) thatcontained AutoML-related discussions. We examine how these topics are spreadacross the various Machine Learning Life Cycle (MLLC) phases and theirpopularity and difficulty. This study offers several interesting findings.First, we find 13 AutoML topics that we group into four categories. The MLOpstopic category (43% questions) is the largest, followed by Model (28%questions), Data (27% questions), Documentation (2% questions). Second, Mostquestions are asked during Model training (29%) (i.e., implementation phase)and Data preparation (25%) MLLC phase. Third, AutoML practitioners find theMLOps topic category most challenging, especially topics related to modeldeployment & monitoring and Automated ML pipeline. These findings haveimplications for all three AutoML stakeholders: AutoML researchers, AutoMLservice vendors, and AutoML developers. Academia and Industry collaboration canimprove different aspects of AutoML, such as better DevOps/deployment supportand tutorial-based documentation.

Challenges and Barriers of Using Low Code Software for Machine Learning