Serverless Evolutionary Computation for Precision Aquaculture: A Cloud-Native Machine Learning Architecture for High-Velocity Marine Sensor Networks

The deployment of precision farming technologies in aquaculture relies on high-density marine sensor networks to monitor volatile environmental parameters such as dissolved oxygen, salinity, and pathogenic blooms. However, processing this high-dimensional, noisy telemetry in real-time presents a severe computational bottleneck. Evolutionary computation and feature selection algorithms are highly effective at optimizing this data, yet their iterative nature introduces profound latency when executed on traditional centralized servers. This paper proposes a cloud-native architectural framework utilizing Amazon Web Services to construct a real-time, serverless machine learning pipeline for precision aquaculture. By deploying asynchronous Python middleware integrated with distributed evolutionary feature selection algorithms, the proposed system ingests high-frequency marine telemetry, autonomously discarding redundant sensor noise before the data reaches the primary inference engine. Preliminary architectural evaluations demonstrate that decoupling evolutionary computation from localized hardware to an elastically scalable cloud infrastructure significantly accelerates real-time anomaly detection, providing data scientists and marine agronomists with a deterministic, highly scalable technological foundation for advancing sustainable aquaculture.