Machine-Learning Operations (MLOps) is maturing into a software-engineering discipline, yet its tiny-scale variant (TinyMLOps)-targeting the resource-constrained microcontrollers embedded in cyber-physical systems (CPS)-remains poorly understood in industrial practice. Opaque models, noisy heterogeneous data, and tight memory budgets hinder adoption in safety-critical settings, where most decisions still rely on human experts. We report a field study of an end-to-end, knowledge-centered TinyMLOps pipeline that fuses domain physics, expert speculation, and sensor streams to deliver explainable, low-footprint models deployable on-device. The pipeline spans automated collection and cleaning of heterogeneous time series, knowledge-driven feature construction, interpretable regularized models, and rolling temporal cross-validation under concept drift. We evaluate it on 4.4 GB of data from two offshore-wind cable-trenching campaigns. The classifier anticipates harmful load peaks up to three minutes ahead at 0.84 AUC within a 32 kB footprint on an ARM Cortex-M4; an ablation shows that injecting prior knowledge halves false alarms and surfaces actionable operational rules. Replaying recommendations in operational dashboards indicates an 11% reduction in non-productive time. We distill engineering lessons and validity threats for trustworthy TinyMLOps in CPS, and release code and an annotated dataset to support reproducibility.

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