Nowadays, most AI workloads are running in Kubernetes, and effectively analyzing and optimizing the performance of large-scale AI workloads remains a significant challenge. We utilize eBPF technology and process dynamic injection techniques to monitor the Pods running the AI workloads. This approach enables online AI profiling that is transparent, non-intrusive, on-demand, and has low overhead for business Pods. We capture data from various aspects such as AI frameworks, GPU kernel functions, GPU library calls, system calls, and CPU utilization, ultimately summarizing and presenting it as a timeline rendering graph. This allows for fine-grained analysis of bottlenecks and potential issues, such as slow training, GPU data transfer delays, or NCCL hang-ups. Additionally, we support profiling for various types of GPU resources to ensure versatility. The online AI profiling solution ultimately determines the optimization direction for GPUs and AI tasks within the Kubernetes cluster.