The resilience and reliability of large-scale AI training platforms are fundamental to enabling contemporary AI innovation and business development. However, with the rapid increase in the scale and complexity of AI model training tasks, anomalies become the norm rather than the exception at scale. Failing to handle them properly may lead to enormous resource waste and prolonged development cycles. Traditional anomaly detection methods struggle to tackle the complex temporal characteristics and extreme class imbalance inherently manifesting in training tasks, and fall short in automated solution to root cause analysis and the follow-up remediation. This paper proposes KAIOps, an end-to-end automated platform solution for anomaly handling in large-scale AI training clusters, and details its implementation for improving AIOps in daily operational maintenance at Kuaishou. KAIOps employs a Temporal Context Encoding mechanism to precisely capture and encode long-term trends and critical temporal context information within fault evolution. The detection model integrates a dynamic class-weighted loss function for enhancing the detection performance. KAIOps further advances these capabilities by integrating knowledge graph and large language model for automated root cause analysis and actionable solution generation, delivering a complete end-to-end intelligent processing pipeline. We conducted a systematic evaluation of KAIOps on the basis of data collected from Kuaishou’s production-grade training clusters and the results show the competitive performance of the proposed approach. KAIOps has been deployed in Kuaishou, in both testbed and production grade environments, consisting of with over 10,000 GPUs, and accelerate the reliability assurance for industry-scale model training and serving.