Conventional error-tolerant strategies for neural networks often rely on redundancy or invasive modifications to normal operation, making them unsuitable for large-scale machine learning (ML) systems composed of complex networks. This paper proposes two complementary perturbation-based solutions that reuse the inference process for cost-effective reliability: Perturbation-Based Error Detection and Correction (PBEDC), which monitors a small set of intermediate “check nodes” and can correct single-bit flips when combined with parity codes, and Concurrent Classifier Error Detection (CCED), which employs a lightweight auxiliary classifier to detect faults in real-time. Evaluations on a Contrastive Language-Image Pre-training (CLIP) model show that PBEDC achieves detection rates exceeding 95% with minimal hardware overhead and scales effectively with network size, while CCED similarly identifies most errors under modest re-computation budgets and imposes negligible runtime cost. These techniques offer a scalable and low-cost framework for dependable operation in contemporary large-scale ML systems.