Guide to Deploying ViT on Kubernetes

Going beyond local Vision Transformer (ViT) deployment, this explains how to scale the model for real production service using Docker and Kubernetes.

The main workflow is as follows:

• Docker Containerization: Package the model serving logic into a container to ensure environment consistency.
• Kubernetes Deployment: Manage the cluster using Google Kubernetes Engine(GKE), handling large-scale traffic through autoscaling and security features.
• TensorFlow Serving Utilization: Serve the model using the SavedModel format, enabling multi-version model management through directory structure configuration.

This approach provides fine-grained control over infrastructure compared to managed services like SageMaker or Vertex AI, and follows an industry-proven standard workflow.