“OPTIMIZING CI/CD PROCESSES FOR AI MICROSERVICES WITH PKONFIG”

This paper explores techniques for streamlining CI/CD workflows in AI-driven microservice environments by leveraging the PKonfig framework.We conduct a comprehensive review of container orchestration and deployment patterns-covering Docker, Kubernetes and Helm-and detail strategies for managing both static and dynamic configuration via PKonfig.A practical methodology is proposed for accelerating image builds through layer parallelization and caching, automating test suites by combining unit tests, AST analysis and LLM-generated prompts, and enforcing immutable images alongside semantic configuration versioning.To sustain high reliability, we introduce a monitoring and analytics stack that incorporates DORA metrics, heatmaps of failure hotspots and ML-based predictive anomaly detection.Our findings demonstrate how to construct a reproducible, scalable and easily roll-backable CI/CD pipeline tailored to frequent AI-component updates.These optimization patterns will be of particular interest to DevOps and MLOps architects responsible for scalable, reproducible and strongly typed dependency management in continuous delivery of models to production-grade, high-load cloud clusters.Additionally, CTOs, R&D leaders and software-engineering researchers seeking formal verification of model-infrastructure updates under observability and compliance constraints will find the approaches valuable.