AI-Augmented DevOps: Leveraging Machine Learning for Predictive Monitoring and Pipeline Optimization

Abstract

The DevOps cross-training model has become widespread in industry, with deep specialization in either produc- tion support or software engineering among teams in a service environment. Software Reliability Engineering (SRE) emphasizes a balance between these disciplines by aiming for minimal technical debt in production systems and aligning ownership with the engineering and product teams responsible for application reliability. Many organizations recognize the importance of Pre- dictive Monitoring to avoid production incidents and the use of Machine Learning for CI/CD optimization, as these can reduce alert noise and deal with the mail problem of Too Many Widgets. However, achieving AI-augmented DevOps requires AI-based Predictive Monitoring for Engineering and Site Reliability Engi- neering (SRE) teams, which covers delivery velocity and resource utilization. AI-augmented DevOps encompasses both Predictive Monitoring to avoid production incidents and Machine Learning- driven Continuous Integration/Continuous Delivery (CI/CD) Op- timization to improve delivery velocity and resource utilization. It is primarily expressed in terms of development and produc- tion environments of Software Development Life Cycle (SDLC) pipelines. These aspects are critical for minimizing Time to Detect (TTD) and Time to Recover (TTR) during incident response, and optimization with respect to Machine Learning models is essential to avoid over-engineering and needless expenses. Information Technology (IT) Decision Makers across all industries prioritize these areas of focus in 2022–2023. AI-augmented DevOps is mainly articulated in terms of DevOps principles and Machine Learning utilization for Predictive Monitoring and CI/CD opti- mizations.