Self-Healing Distributed MLOps: Autonomous Fault Detection Node Eviction and Resilient State Reconciliation

Large-scale distributed training of deep vision architectures is vulnerable to execution pathologies, including gradient explosion, corrupted mini-batch NaN injection, spatial Grad-CAM saliency collapse, parametric weight variance dispersion, and storage I/O deadlocks. In standard MLOps pipelines, such failures cause silent trial divergence, wasted GPU compute, and protracted manual triage. We propose Self-Healing MLOps, an autonomous closed-loop orchestration and repair architecture operating over the Model Context Protocol (MCP) to monitor, diagnose, and remediate deep learning runtime pathologies in real time. Embedded within the NeuralForgeAI cluster, an autonomous LLM observer ingests continuous telemetry streams—tracking loss trajectories, weight variance, and Grad-CAM foreground saliency ratios via FastMCP endpoints. When numerical instability or spatial attention collapse is detected, the agent executes targeted causal interventions: checkpoint rollbacks to MinIO S3 storage, dynamic learning rate annealing, poisoned sample isolation, backbone layer freezing, or worker respawning. Across 25 experimental failure injection runs spanning five distinct fault modes, the self-healing observer achieves an average anomaly detection latency of 2.52 ± 0.14 s and 100% unattended recovery without human operator intervention, preserving target detection fidelity (0.612 ± 0.005 mAP50-95) and slashing wasted GPU-hours by 99.9% compared to unmonitored baselines.