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Sixth post in the series. In the previous one, we automated GPU cluster provisioning. Now let’s talk about what happens after the hardware is ready: how a model goes from “works on my notebook” to “running in production with an SLA.” The model with no birth certificate A data scientist drops a message in the team channel with a link to a shared drive: “Here’s the model. It’s a 15 GB PyTorch checkpoint. We need it in production by Friday.” ...

Fifth post in the series. In the previous one, we dove inside the GPU. Now let’s automate everything around it. Because understanding GPUs is half the battle; provisioning them consistently and at scale is where infrastructure engineering actually meets AI. The $4,000 typo I started the week with a win. Manually provisioned a GPU cluster in East US 2 for an ML experiment: AKS with a Standard_NC6s_v3 node pool, accelerated networking, NVIDIA drivers, correct taints. Took almost a full day, but it worked. ...