Serverless AutoML for High-Velocity Data Streams: Dynamic Hyperparameter Optimization in Cloud-Native Continuous Learning Pipelines
The application of Automated Machine Learning to infinite, high-velocity data streams represents a critical frontier in Big Data Science. Traditional hyperparameter optimization frameworks, such as grid search or Bayesian optimization, are inherently designed for static, batch-learning environments. When applied to non-stationary data streams, these techniques introduce severe computational latency, stalling ingestion pipelines and rendering models obsolete as concept drift occurs. This paper proposes a cloud-native architectural framework utilizing Amazon Web Services to construct a real-time, serverless pipeline for continuous AutoML. By deploying distributed Python middleware integrated with asynchronous hyperparameter evaluation algorithms, the proposed system ingests high-frequency telemetry while spawning isolated, ephemeral compute nodes to continuously evaluate competing hyperparameter configurations in parallel. Preliminary architectural evaluations demonstrate that decoupling the data stream ingestion from the dynamic hyperparameter optimization layer significantly accelerates model adaptation without interrupting continuous inference. This methodology provides artificial intelligence researchers with a deterministic, highly scalable technological foundation for optimizing machine learning algorithms on massive data streams.