Cloud-Native Lakehouse Architectures and Multi-Task Learning for Industrial Prognostics

Abstract

The rapid accumulation of high-frequency telemetry across advanced manufacturing lines has exposed structural bottlenecks within conventional enterprise architectures, challenging real-time equipment prognostics and health management. This critical review examines the strategic integration of cloud-native lakehouse architectures and multi-task learning paradigms for synchronized anomaly detection and remaining useful life estimation. Evidence indicates that decoupled storage-compute topologies mitigate cross-silo latency and computational costs to some extent; however, persistent query latency under stochastic burst telemetry reveals unresolved cost-performance trade-offs. Concurrently, multi-task deep architectures enhance predictive generalization by exploiting latent degradation correlations, though negative transfer across disparate operational modes presents empirical ambiguity. Discrepancies between sanitized offline benchmarks and shop-floor data streams suggest reported improvements might partially reflect feature overfitting. Considering these infrastructural and algorithmic constraints, this leads us to further thinking that prognostic modeling requires co-optimizing underlying distributed storage topologies. Further research is needed to construct elastic, hardware-aware frameworks across heterogeneous environments.