A Systematic Review of Real-Time Adaptive Automation in Machine Learning Model Training
Keywords:
Real-time machine learning, adaptive automation, dynamic model training, online learning, automated hyperparameter tuning, concept drift, real-time analytics, continual learning, ML pipelinesAbstract
Real-time adaptive automation is rapidly transforming machine learning (ML) model training by enabling systems to make dynamic adjustments in response to evolving data, environmental changes, and performance feedback. This systematic review explores current advancements, architectures, and techniques that support adaptive automation during ML training, including dynamic hyperparameter tuning, online learning, automated data preprocessing, resource-aware training, and feedback-driven model optimization. By synthesizing findings from recent studies across domains such as autonomous systems, cybersecurity, industrial IoT, and real-time analytics, this review highlights the strengths and limitations of existing approaches. Key challenges—including concept drift, latency constraints, scalability issues, and limited interpretability—are examined in detail. The results indicate that real-time adaptive automation significantly improves model accuracy, responsiveness, and robustness in dynamic environments. However, standardized frameworks, benchmarking protocols, and unified evaluation metrics are needed to fully harness its potential. This review provides foundational insights and future research directions for designing next-generation adaptive ML training systems.
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