A Real-Time Monitoring Framework for Deployed Machine Learning Models with Drift Detection and Performance Analytics

Ganga Vijay Kumar; B.Pramodhini

doi:10.64643/ijirtv12i12-201193-459

May 7, 2026International Journal of Innovative Research in TechnologyOpen Access

A Real-Time Monitoring Framework for Deployed Machine Learning Models with Drift Detection and Performance Analytics

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Key Points

This research aims to develop a framework for real-time monitoring of machine learning models to detect drift and analyze performance.
Implemented a monitoring framework for deployed machine learning models.
Integrated drift detection mechanisms to assess model performance.
Evaluated effectiveness through performance analytics.
The framework successfully detected drift in model performance over time.
Performance analytics indicated improved accuracy and responsiveness to data changes.
User feedback suggested high satisfaction with the monitoring tools.

Implication

Evaluates effectiveness of a monitoring framework in machine learning, suggesting improved performance analytics.

Abstract

Explore the article titled A Real-Time Monitoring Framework for Deployed Machine Learning Models with Drift Detection and Performance Analytics from IJIRT Volume 12, Issue 12. This study evaluates the effectiveness of teaching programs on waste management knowledge among women.

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Cite This Study

Kumar et al. (2026) studied this question.

synapsesocial.com/papers/6a21164cd499ed480b16f49b https://doi.org/10.64643/ijirtv12i12-201193-459

View Full Paper

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A Real-Time Monitoring Framework for Deployed Machine Learning Models with Drift Detection and Performance Analytics

Key Points

Implication

Abstract

Discussion

Cite This Study

A Real-Time Monitoring Framework for Deployed Machine Learning Models with Drift Detection and Performance Analytics

Key Points

Implication

Abstract

Discussion

Cite This Study

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