Autonomous Production Operations: AI-Driven Incident Management and Self-Healing Data Platforms for Banking Systems

Abstract

This operational landscape of thousands of batch jobs and streaming applications of different types‚ including tier-1 workloads for regulatory compliance‚ fraud detection‚ and customer experience‚ means that customary operational models of post-event remediation‚ human-driven triage‚ root-cause analysis‚ prediction‚ and manual change management are neither scalable nor reliable enough in the production environments of modern platforms․ We present a framework for AI-assisted, automated production operations, including incident prediction, root cause analysis, smart remediation, and platform optimization. The framework leverages deep learning to detect anomalies and predict failures from operational telemetry, causal inference to automate root cause analysis and create a knowledge graph, reinforcement learning to identify optimal mitigation and remediation, NLP to automatically initialize and execute runbooks, and an operations outcomes-based self-learning capability. The framework has been applied to banking data systems conducting tier-1 transactional processing with millions of records per day and has resulted in a substantial increase in detection speed and incident prevention․ Incident remediation from alerts is fully automated․ SLA attainment and operating costs have reduced due to automation․ The book proposes a new model of production operations to move organizations away from incident response and towards reliability engineering.