The jump from a local model deployment to something that can serve real users is where most ML projects hit a wall. This walkthrough shows how to containerize a Hugging Face Vision Transformer (ViT) with TensorFlow Serving and deploy it on Google Kubernetes Engine, using the same battle-tested workflow that powers production systems across the industry.

The process starts with Docker. You take the SavedModel from your local training run, place it in the directory structure TensorFlow Serving expects—<MODEL_NAME>//—and copy it into a running tensorflow/serving container. Then you commit that container as a new image. One detail worth flagging: the guide recommends using an optimized TensorFlow Serving build that leverages hardware instruction sets like AVX512 if you know your deployment target. Inference speed on deep learning models can shift dramatically depending on whether those optimizations are present.

From there, the containerized model moves to Kubernetes. The post uses GKE for cluster management, though the authors note the concepts apply equally to Minikube for local testing. Kubernetes brings autoscaling and security features that a bare Docker container can’t provide on its own.

The authors address the elephant in the room directly: why bother with this setup when SageMaker or Vertex AI offer ML-specific deployment features out of the box? Their answer is that the Docker-plus-Kubernetes workflow has been battle-tested for years, gives teams granular control over deployments, and abstracts away the non-trivial infrastructure bits without locking you into a specific cloud vendor’s ML platform. That tradeoff—more setup work for more portability—is one every ML engineering team has to weigh.