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 the increasing adoption of machine learning (ML) models and systems inhigh-stakes settings across different industries, guaranteeing a model'sperformance after deployment has become crucial. Monitoring models inproduction is a critical aspect of ensuring their continued performance andreliability. We present Amazon SageMaker Model Monitor, a fully managed servicethat continuously monitors the quality of machine learning models hosted onAmazon SageMaker. Our system automatically detects data, concept, bias, andfeature attribution drift in models in real-time and provides alerts so thatmodel owners can take corrective actions and thereby maintain high qualitymodels. We describe the key requirements obtained from customers, system designand architecture, and methodology for detecting different types of drift.Further, we provide quantitative evaluations followed by use cases, insights,and lessons learned from more than two years of production deployment.

Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models