From drift to adaptation to the failed ml model: Transfer Learning in Industrial MLOps

2026-02-01

AI Summary

In industrial MLOps, machine learning models often degrade due to data drift yet lack systematic mechanisms for timely updates. Addressing this challenge, this work proposes and systematically evaluates three transfer learning strategies—Ensemble Transfer Learning (ETL), All-Layer Transfer Learning (ALTL), and Last-Layer Transfer Learning (LLTL)—for updating degraded feedforward neural networks under varying data batch sizes. Experimental results demonstrate that ETL achieves the highest prediction accuracy in small-batch scenarios (e.g., 5-day intervals), whereas ALTL performs better in larger-batch settings (e.g., 8-day intervals). This study provides empirical evidence and practical guidance for selecting efficient, adaptive model update strategies in real-world industrial environments, thereby enhancing model robustness and longevity amid evolving data distributions.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Humans and AI: Human-in-the-loop Machine Learning
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Model adaptation to production environment is critical for reliable Machine Learning Operations (MLOps), less attention is paid to developing systematic framework for updating the ML models when they fail under data drift. This paper compares the transfer learning enabled model update strategies including ensemble transfer learning (ETL), all-layers transfer learning (ALTL), and last-layer transfer learning (LLTL) for updating the failed feedforward artificial neural network (ANN) model. The flue gas differential pressure across the air preheater unit installed in a 660 MW thermal power plant is analyzed as a case study since it mimics the batch processes due to load cycling in the power plant. Updating the failed ANN model by three transfer learning techniques reveals that ETL provides relatively higher predictive accuracy for the batch size of 5 days than those of LLTL and ALTL. However, ALTL is found to be suitable for effective update of the model trained on large batch size (8 days). A mixed trend is observed for computational requirement (hyperparameter tuning and model training) of model update techniques for different batch sizes. These fundamental and empiric insights obtained from the batch process-based industrial case study can assist the MLOps practitioners in adapting the failed models to data drifts for the accurate monitoring of industrial processes.

Problem

• data drift
• model failure
• MLOps
• model adaptation
• industrial processes

Innovation

• Transfer Learning
• MLOps
• Data Drift
• Model Adaptation
• Industrial AI