AI-powered Self-healing Systems for Fault Tolerant Platform Engineering: Case Studies and Challenges

This paper explores the paradigm of AI-powered self-healing systems within the context of fault-tolerant platform engineering. As systems become increasingly complex, the ability to autonomously detect and address faults is paramount for ensuring continuous operation and reliability. Through a series of case studies, this research examines the application of AI techniques such as machine learning and neural networks in creating self-healing mechanisms. Challenges such as scalability, adaptability, and robustness are analyzed alongside practical implementations. The findings contribute to advancing the understanding of AI's role in enhancing fault tolerance and resilience in engineering platforms.