The manufacturing industrial internet (MII) is transforming traditional factories into data-driven environments. However, the dynamically varying contexts of the MII, caused by adjustment of process parameters, equipment degradation, and customized specifications, challenge the deployed machine learning models used in process modeling, variation analysis, and anomaly detection. To address this, we propose a novel approach for robust machine learning pipeline selection and adaptation in varying industrial contexts. We introduce a weighted ensemble mechanism based on Bayesian latent space model recommender systems, optimizing sparse ensemble weights across pipelines while incorporating uncertainty quantification. This enables data-driven decision making under uncertainty by automatically selecting and adapting optimal pipelines, reducing manual intervention and improving computational efficiency. We validate our methodology using real-world data from two manufacturing processes (fused deposition modeling and aerosol jet printing) and one chemometric data set (from Tecator). Results demonstrate that our approach achieves superior and more robust performance across data sets compared with traditional single-pipeline recommenders, highlighting the importance of uncertainty quantification in improving pipeline selection accuracy and robustness. History: Bianca Maria Colosimo served as the senior editor for this article. Funding: This work was supported by the National Science Foundation Awards DMS-2413701, DMS-2124535, CMMI-2331985, and Grant CMMI-2430998 and the American Heart Association Collaborative Science Award 23CSA1052735. Data Ethics & Reproducibility Note: The code capsule is available on Code Ocean at https://codeocean.com/capsule/4351450/tree/v1 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2024.0052 ).
Chen et al. (Mon,) studied this question.