MLOps-Driven Software Engineering: Designing Feedback-Loop Architectures for Intelligent Applications

Abstract

Intelligent applications provide long-term value when learning becomes part of a systematically engineered and reaction-driven review. The proposed paper presents an MLOps motivated reference architecture that considers data, models, and decisions to be versioned and observable and auditable resources where continuous training, online experimentation, and closed-loop monitoring are combined with explicit SLIs/SLOs. The architecture integrates Kafka/Flink ingestion, point-in-time feature stores, model registries, progressive delivery (shadow→canary→blue/green) implemented with statistical gates and promotes and automatically rolls back in case of drift or latency violation. The approach is shown in two case studies that are production-oriented: an e-commerce recommender and a real-time fraud detector. In-the-field, feature session-sequence and merchant-graph are better than AUROC/PR-AUC and reduce calibration error by half through temperature scaling. The recommender recommends to the online world with a +3.2% CTR lift (p<0.01) at p95 latency of a 120 ms SLO, and the fraud system with fewer false positives at constant TPR 0.82 and lower incident rates; bandit tuning results in an extra +0.6 CTR. End-to-end observability and policies on burn rate reduce MTTR by hours to one hour, and fairness guard policies ensure ΔTPR ≤0.05 per segment. These findings transform interactions into data, data learning, and least unsafe, cost-conscious releases, re-framing ML as an SRE-operated service as opposed to the best-effort experimentation. The blueprint can be replicated, audited, and cost-sensitive, and will allow incremental implementation across heterogeneous enterprise stacks, clouds, and groups.