Self-Healing AI Pipelines for Real-Time Financial Integration in Cloud-Native Systems
Keywords:
AI-Enabled DevOps, AI Governance Frameworks, Continuous Compliance for AI, Risk Management in AI Systems, Cloud-Native AI Pipelines, AI Model Lifecycle Management, MLOps and DevOps Integration, CI/CD for AI Systems, Self-Governing AI Pipelines, Financial Systems Integration, SaaS Ontology-Based Development, No-Code AI Development, AI Deployment Automation, DevOps Toolchains for AI, AI Operational Resilience, Responsible AI Practices, AI Risk Mitigation Strategies, Enterprise AI Integration, Intelligent Automation Pipelines, AI Engineering Practices.Abstract
Artificial Intelligence (AI)-enabled capabilities revolutionize today’s businesses and lead to the creation of numerous AI-powered services. Consequently, organizations also seek practical ways to integrate AI technologies into their operations. AI models must be constantly and rigorously governed to ensure continuous compliance, mitigate risks, and avoid excessive business disruptions. Several AI-powered services have been deployed worldwide, enhancing many of them, especially financial ones. Yet, it is challenging to build and deploy a DevOps pipeline for AI-powered services in a completely automated and self-governing manner—mainly due to lack of software development resources with adequate profiles, asymmetries with high-cloud-native areas supporting AI modeling, and the urgency of crisis-sensitive environments demanding immediate responses.
Building and running AI-powered DevOps pipelines for financial system integration in cloud-native environments can benefit from practical operational advice covering human, technology, and process aspects. The areas to address derive from practical evidence obtained during the development of a reference architecture and empirical case studies that experimented with a SaaS ontology to develop complex systems in a strictly no-code way. The logic behind them stems from operations absorbed in other models, but applied back to TF-DevOps. The guidance explores the necessary talent profile—essentially, individuals in control—technology, toolchains, standards, and techniques with sufficient coverage to ameliorate the inherent CI/CD asymmetries. It can be used to complement and assess industry literature on AI-powered DevOps implementations.
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