Potential of AI and ML to Enhance Error Detection, Prediction, and Automated Remediation in Batch Processing
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I4P108Keywords:
Batch Processing, Machine Learning, Error Detection, Predictive Maintenance, Automated Remediation, Fault Tolerance, Anomaly DetectionAbstract
A batch processing system is especially critical to a number of data-dependent and mission-sensitive functions in markets including financial, healthcare, and supply chain management. Such systems, however, are very vulnerable to run-time errors, performance degradation, and system collapses, as they depend on sequential task completion, contemporaneous scheduling, and have few mechanisms for real-time feedback. The drawback with the traditional rule-based monitoring and manual human interventions is that they are unable to detect some fine-scale variations or anticipate failures beforehand, leading to operational downtimes and wastages of resources. The criticality of next-generation solutions that can be used to facilitate the transformation of the market is discussed in this paper with regard to Artificial Intelligence (AI) and Machine Learning (ML) abilities to expand the range of processes related to batch processing by helping to identify, model, and resolve errors on a preventative basis. We introduce a unified framework that employs both unsupervised and supervised learning models to ensure that batch processing environments are more resilient and autonomous and, therefore, cannot fail easily. The approach involves preprocessing the log data, identifying patterns, and training models. A history of past flawed executions is used to identify and predict failures before they happen, thereby averting them to prevent the failure. The main results of our prototype implementation demonstrate a considerable increase in the accuracy of detecting errors, providing warnings in advance, and the efficiency of system recovery compared to traditional systems. The flexibility of AI-based remediation agents to automatically correct mistakes efficiently with little to no human touch is also evident in our study, which in benchmark cases lowered Mean Time To Recovery (MTTR) by as much as 40 percent. The results highlight the feasibility of implementing AI/ML in actual batch operations to reduce the downtime, optimize resource use and maximize Service-Level Agreement (SLA) achievements. This study provides guidance on constructing smart, self-healing batch systems that can learn throughout their operation and self-improve in the future
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