Posts

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: ...

Had a 1:1 last week. Nothing unusual, just a regular conversation, catching up on things. But there was one moment that stayed with me after the call ended. At some point he said he usually thinks about people in four different ways. Not as an official framework or anything like that, just how he personally sees it. And I don’t know, the way he explained it felt simple, but it kind of stuck. ...

Over the past year, I’ve been working closely with some of the bigger and more visible AI customers in Microsoft’s ecosystem. Large platforms. Fast-moving teams. High expectations. High stakes. On paper, that kind of visibility sounds exciting. In reality, it comes with a weight that’s hard to explain unless you’ve been there. Because being close to impact also means being close to consequence. Visibility changes everything When you work with smaller teams or early-stage projects, mistakes are usually contained. You can recover. You can explain. You can iterate. ...

…and why that’s not an accident. If you have worked with both Azure and AWS long enough, you have probably felt it. AWS feels straightforward. Azure feels… heavier. Not worse. Not broken. Just harder to reason about. The console feels denser. The mental model feels less obvious. The number of “extra” concepts feels higher. This is not a beginner problem. Senior engineers feel it too. And the most interesting part is this: that friction is not accidental. ...

For years, “cloud-first” has been treated as a badge of honor. Companies proudly announce that everything is in the cloud, architects optimize for migrations instead of outcomes, and teams equate progress with how little infrastructure they still own. But after working with dozens of real systems, across different industries and at different scales, one thing becomes clear. Cloud maturity is not about being 100% cloud. It is about knowing why each workload is where it is. ...

Throughout my career, I’ve had countless conversations about career paths with colleagues, mentees, and professionals in transition. In those talks, I often share practices that have helped me personally along the way. But this article is different. It’s transparent and honest. Although I’ve spoken about parts of my story with people close to me, this is the first time I’m sharing it so openly, including my mistakes, lessons, and reflections in a public space. ...