Owing to the rapid development of artificial intelligence (AI) technologies and the efficient deployment of AI applications on cloud-native platforms, an increasing number of developers and internet companies deploy AI applications on Kubernetes clusters. However, Kubernetes is not designed chiefly for deep learning, which, as a special field, requires customized optimization. This study designs and implements a series of optimization schemes, mainly from the perspectives of data processing, graphics processing unit (GPU) calculation, and distributed training that deep learning requires, for the scenario of deploying deep learning workloads on Kubernetes clusters of a certain scale. The proposed optimization schemes involve data processing and calculation. These technologies reduce the difficulty in deploying AI workloads on large-scale cloud-native platforms and improve operational efficiency greatly. Moreover, the practice also verifies their significant improvement effect on AI applications.