A Cloud-Native MLOps Framework for Drift Detection, Automated Retraining, and Reliable Real-Time Inference in Predictive AI Systems
Deployed predictive machine learning models inevitably degrade when real-world production data deviates from training distributions. Static deployment strategies cannot handle this environmental shift, causing drops in accuracy and forcing engineering teams to rely on slow, manual retraining audits. This paper presents...