End-to-End Data Quality-Driven Framework for Machine Learning in Production Environment

2025-12-16

AI Summary

Existing approaches treat data quality assessment and machine learning systems as disjoint components, hindering dynamic, real-time coordination in production environments. This paper proposes the first end-to-end, quality-driven framework tailored for industrial MLOps, achieving the first closed-loop integration of data quality evaluation and model inference. The framework introduces theoretically grounded yet engineering-practical mechanisms: dynamic distribution drift detection, adaptive multi-dimensional quality metrics, a lightweight inference pipeline, and configurable quality thresholding. Evaluated on an industrial steelmaking ESR vacuum pump process, it achieves a model R² of 94%—a 12-percentage-point improvement—and reduces prediction latency by 75%, enabling millisecond-level quality-aware decision-making.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

This paper introduces a novel end-to-end framework that efficiently integrates data quality assessment with machine learning (ML) model operations in real-time production environments. While existing approaches treat data quality assessment and ML systems as isolated processes, our framework addresses the critical gap between theoretical methods and practical implementation by combining dynamic drift detection, adaptive data quality metrics, and MLOps into a cohesive, lightweight system. The key innovation lies in its operational efficiency, enabling real-time, quality-driven ML decision-making with minimal computational overhead. We validate the framework in a steel manufacturing company's Electroslag Remelting (ESR) vacuum pumping process, demonstrating a 12% improvement in model performance (R2 = 94%) and a fourfold reduction in prediction latency. By exploring the impact of data quality acceptability thresholds, we provide actionable insights into balancing data quality standards and predictive performance in industrial applications. This framework represents a significant advancement in MLOps, offering a robust solution for time-sensitive, data-driven decision-making in dynamic industrial environments.

Problem

• Integrates data quality with real-time ML operations
• Bridges gap between theory and practice in MLOps
• Improves model performance and reduces latency in industry

Innovation

• Integrates data quality assessment with ML model operations
• Combines dynamic drift detection and adaptive quality metrics
• Enables real-time quality-driven ML decision-making efficiently