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mlinfra digest · 15.04.2026
Hi everyone!
#mlinfra_digest
As I go through recent material on AI/ML infrastructure, I keep getting the same feeling more and more: the main difficulty has long stopped being about spinning up a model, it's about managing all of it properly in production afterward.
1️⃣ Experimenting with GPUs: GKE managed DRANET and Inference Gateway AI Deployment
A good piece from Google on DRANET and Inference Gateway as a separate platform layer for AI workloads.
Plus, Inference Gateway is no longer just an idea from vendor blog posts, it's also an extension of the Kubernetes Gateway API that you actually want to test hands-on.
https://cloud.google.com/blog/topics/developers-practitioners/experimenting-with-gpus-gke-managed-dranet-and-inference-gateway-ai-deployment
https://github.com/kubernetes-sigs/gateway-api-inference-extension
2️⃣ Unlock efficient model deployment: Simplified Inference Operator setup on Amazon SageMaker HyperPod
What I liked at AWS is the inference operator approach itself. If you want to write your own operator for an inference platform, it's worth seeing how they've broken it down: lifecycle, rollout, and platform-style management of the serving layer. Especially useful if KServe doesn't fit your needs for some reason and you want to build a more custom control plane.
https://aws.amazon.com/blogs/architecture/unlock-efficient-model-deployment-simplified-inference-operator-setup-on-amazon-sagemaker-hyperpod/
3️⃣ Overcoming inference challenges
What I liked at Red Hat is a simple and very true-to-life point: the first successful response from a model isn't a win, it's just the start. What follows is Day 2, where you have to deal with the hardware-model Tetris, latency/throughput trade-offs, cost, and GPU utilization. And that's exactly where it quickly becomes clear whether you have an inference platform or just a pile of YAMLs.
https://www.redhat.com/en/blog/overcoming-inference-challenges