SRE or Scalable Site Reliability Engineering practices prioritise integrating software engineering norms and maintaining extremely scalable and reliable software systems. The aim is to establish actionable insights to decrease the gap in reliability between conventional software systems and emerging ML-driven infrastructures. The traditional SRE models did not highlight ML-specific issues, such as data drift, model degradation, and inference latency specifically. The outcomes reveal the essential reliability measures, analyse the state-of-the-art observability architectures, and observe integration risks with the help of a mixed-method technique that consists of case studies and performance graphs. This paper investigates SRE initiatives to increase monitoring and alerting of production ML methods. The analysis in this paper influences the ML-aware diagnostic, automation, and adaptive alerting system. Furthermore, applications of ML-aware SRE, automated monitoring, and others were recommended in this paper

The study focuses on creating an integrated healthcare system across different clouds using MuleSoft and Kubernetes to support better operation and use of data. An explanatory design is used, and also, both qualitative and quantitative analyses have been conducted. These technologies are found to enhance patient care and boost healthcare operations, overcoming issues like handling compliance risks and matching systems from the past. According to the study, it is important to set rules for secure API use, run both public and private cloud services, and increase workers’ expertise. Future projects should consider using blockchain to help healthcare systems work well across numerous clouds