Summary of A Scalable Approach to Covariate and Concept Drift Management Via Adaptive Data Segmentation, by Vennela Yarabolu et al.
A Scalable Approach to Covariate and Concept Drift Management via Adaptive Data Segmentation
by Vennela Yarabolu, Govind Waghmare, Sonia Gupta, Siddhartha Asthana
First submitted to arxiv on: 23 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the issue of data drift in continuous machine learning (ML) systems, where discrepancies between training and test data lead to significant performance degradation and operational inefficiencies. The authors contend that incorporating drifted data into model training enhances accuracy and robustness. They introduce an advanced framework that integrates data-centric approaches with adaptive management of covariate and concept drift. The framework employs sophisticated data segmentation techniques to identify optimal data batches for training, ensuring models remain relevant over time. Experimental results on real-world and synthetic datasets show improved model accuracy while reducing operational costs and latency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how machines can learn from new data without getting worse at it. When we train machines using old data, they might not work well when they see new data that’s a little different. This is called “data drift”. The authors of this paper have come up with a way to make machines learn better by including the new data in their training. They use special techniques to pick out the best parts of the old data and use them to improve the machine’s learning. This makes the machine more accurate and efficient, which is important for big systems that need to process lots of data quickly. |
Keywords
* Artificial intelligence * Machine learning
