PyTorch vs TensorFlow in Modern Production Microservices

It feels like PyTorch has completely won the research and developer mindshare community, especially for custom computer vision and generative pipelines. However, TensorFlow Serving still has robust enterprise deployment tooling.

For those running containerized AI microservices on Docker and Kubernetes, which ecosystem do you find easier to maintain in terms of ONNX exporting, container weight optimization, and CI/CD integration?