AN INVESTIGATION OF THE CURRENT STATE AND PROSPECTIVE POSSIBILITIES FOR MACHINE LEARNING IN RELIABILITY ENGINEERING AND SAFETY APPLICATIONS

With an emphasis on improving Site Reliability Engineering (SRE) practices, this paper explores the present status, efficacy, and future possibilities of Machine Learning (ML) in reliability engineering and safety-critical operations. The paper looks at the current state of the art in using ML techniques to enhance operational resilience and avoid system failures. These techniques include automated decision-making, anomaly detection, and predictive analytics. The researcher evaluates current frameworks, such as AI for IT Operations (AIOps), and point out their advantages and disadvantages in terms of their capacity to back dependability and safety goals. Data quality difficulties, restricted interpretability, integration hurdles, legal impediments, talent shortages in the workforce, and a lack of domain-specific models are some of the important gaps, hazards, and restrictions that the investigation highlights as impeding the effective integration of ML. Notwithstanding these obstacles, the research highlights large openings for innovation, including the implementation of explainable AI, hybrid modelling methodologies, connection to IoT systems, and improved real-time monitoring capacities. A deliberate, staged strategy that covers technological, organisational, and regulatory factors is necessary for the effective deployment of ML, which the data indicate is a potent enabler for reliability and safety engineering. With the help of ML, SRE might become a field that is more predictive, adaptive, and robust by filling in gaps and capitalising on possibilities.