Google Cloud published a blog post discussing best practices for data loading for AI/ML inference on GKE. As AI models grow in sophistication, increasingly large model data is needed to serve them. Loading the models and weights along with necessary frameworks to serve them for inference can add seconds or even minutes of scaling delay, impacting both costs and the end-user experience. This blog explores techniques to accelerate data loading for both inference serving containers and downloading models + weights, so you can accelerate the overall time to load your AI/ML inference workload on Google Kubernetes Engine (GKE).