Deploy HuggingFace ViT on Kubernetes using TensorFlow Serving (GKE)

AI Impact Summary

The post outlines an end-to-end pattern for deploying a Vision Transformer (ViT) model from Hugging Face Transformers on Kubernetes using TensorFlow Serving, including packaging the SavedModel, building a custom TF Serving image, and running on GKE with gRPC and REST endpoints. It emphasizes versioned model deployment via the SavedModel structure (e.g., /models/hf-vit/1) and notes hardware-optimized TF Serving builds (AVX512) to maximize inference throughput. This enables scalable, multi-user CV inference in production, but introduces ops overhead for Docker image customization, image publishing to GCR, and managing the GKE cluster and deployment manifests.