Short, honest write-ups of the ideas that come up in interviews and on the job. Each one teaches the concept properly, cites the Microsoft documentation, and points back to the class that drills it until it sticks.
What a 429 actually means — TPM quota per region and model, with max_tokens reserved at request time — and the fixes in order: honor Retry-After, back off with jitter, right-size max_tokens, then buy capacity to the measurement.
Microsoft's platform for building, deploying, and operating AI apps and agents on Azure. What it is versus Azure OpenAI and Azure ML, serverless versus managed-compute deployment, the model catalog and Agent Service, and the…
Azure GPU VMs by family: NC-series for inference and smaller training, ND-series (like ND H100 v5) for large distributed training over InfiniBand, NV-series for visualization. How GPUs, interconnect, quotas, spot capacity, and…
The best cloud stack for vibecoding and AI-first prototyping is the one with the least IAM friction and the fastest path to a live URL — a zero-config backend plus a push-to-deploy host. Here's the stack, the table, and the migration cliff nobody warns you about.
You do not need to code to ship something with AI on Azure. In about an hour you will have an assistant grounded on your own PDFs in AI Foundry — and an honest "Azure AI infrastructure" line for your resume.
The AI-103 credential for developers building AI apps and agents on Azure AI Foundry: the build-deploy-monitor loop, prerequisites, and how to study. Confirm the live outline on Microsoft Learn.
The stages stay the same; the bottleneck moves from typing syntax to guiding and reviewing. Why CI/CD, tests, and scanning become the safety nets.
Describe intent in plain language, an AI writes and revises the code, you steer and review. Where the term came from, the workflow, and the honest limits.
AI drafts the boilerplate IaC, first-pass runbooks, and log triage; the architecture, root-cause, and accountability stay yours.
Add a GPU node pool, let pods request GPUs, autoscale to zero when idle. When Kubernetes beats raw VMs for AI — and when it's overkill.
Two ways an AI job stalls: a slow GPU interconnect and storage that can't feed them. InfiniBand plus a parallel file system keep the expensive silicon busy.
AI infrastructure is ordinary cloud engineering pointed at GPUs — GPU VMs, the model platform, orchestration, cost — and one of the least-saturated, highest-demand niches in 2026. What it is, and how you get in.
A language model is confident and often wrong about your data, because it never saw it. Azure AI Search finds the right passages from your own content and hands them to the model — so the answer is grounded, not guessed.
One short, honest Azure note at a time — plus the occasional hiring signal. No spam, no card, unsubscribe in one click.