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data quality monitoring

2 articles · Page 1 of 1

Jul 23, 2026 · Artificial Intelligence

Operating High-Quality Datasets as Continuous Data Products for AI

This article presents a six-step framework for operating high-quality datasets as continuous data products, covering responsibility assignment, version baselines, quality and AI effect monitoring, feedback-to-candidate pipelines, controlled release strategies, and retirement mechanisms to ensure datasets evolve with business, models, and risk boundaries.

AI data productsAgentOpsDataOps

0 likes · 14 min read

Operating High-Quality Datasets as Continuous Data Products for AI

NetEase Cloud Music Tech Team

Nov 18, 2022 · Artificial Intelligence

Machine Learning-Based Anomaly Detection for Core Business Metrics

The paper proposes a containerized, machine‑learning framework that fuses rule‑based and XGBoost‑driven anomaly detection to monitor daily active users on a cloud music platform, achieving 89 % recall, 81 % precision and up to 74 % recall improvement over traditional threshold methods, while outlining future model refinement and broader metric applicability.

3-sigmaBusiness MetricsHolt-Winters

0 likes · 11 min read

Machine Learning-Based Anomaly Detection for Core Business Metrics