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

The distribution gap between training datasets and data encountered inproduction is well acknowledged. Training datasets are often constructed over afixed period of time and by carefully curating the data to be labeled. Thus,training datasets may not contain all possible variations of data that could beencountered in real-world production environments. Tasked with building anentity resolution system - a model that identifies and consolidates data pointsthat represent the same person - our first model exhibited a cleartraining-production performance gap. In this case study, we discuss ourhuman-in-the-loop enabled, data-centric solution to closing thetraining-production performance divergence. We conclude with takeaways thatapply to data-centric learning at large.

Combining Data-driven Supervision with Human-in-the-loop Feedback for Entity Resolution