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. ...
Fourth post in the series. In the previous one, you learned which GPU VMs to provision and how to connect them. Now we’re going to look inside the GPU to understand what happens at the silicon level. Not to write CUDA kernels, but to be a better troubleshooter and have informed conversations with the ML team. The 2 AM ticket Slack fires at 2 AM. The ML team’s training job crashed again. The error is a single line: ...
Third post in the series where I translate AI into the language of those who live and breathe infrastructure. In the previous post, we talked about the hidden storage bottleneck. Today we’re going to what everyone thinks is the main topic of AI: compute. Spoiler: it’s not just about having the most expensive GPU. It’s about having the right GPU, connected the right way. The story you don’t want to live The ML team asks for “a GPU cluster for training.” You do what any infra engineer would: provision eight Standard_D16s_v5 VMs. Sixty-four vCPUs each, 128 GiB of RAM, premium SSD. On paper, plenty of power. ...
This is the second post in the series where I translate AI into the language of infrastructure engineers. In the first post, I showed that AI is just another workload and that your infra skills already prepare you more than you think. Now let’s talk about the bottleneck that everyone ignores — the hidden villain behind performance issues in virtually every AI project I’ve seen: storage. The midnight call You did everything right. The ML team asked for a GPU cluster and you delivered: eight NVIDIA A100s across two nodes, high-bandwidth networking, CUDA drivers up to date. Flawless deployment. The team kicked off their first training job Friday at 6 PM and you went home feeling good. ...
This is the first post in a series where I’ll translate the world of AI into the language that infrastructure engineers already speak. If you’re the kind of professional who configures VMs, builds CI/CD pipelines, and gets woken up at 2 AM when Nagios fires, this content is for you. The series is based on my open-source book AI for Infrastructure Professionals, adapted and expanded here on the blog. The Monday morning message It’s 8:47 AM on a Monday. You’re halfway through your coffee, reviewing a Terraform plan for a network redesign, when a Slack message lights up your screen. It’s from the data science team lead: ...