Stop watching Kubernetes tutorials. Build, serve, and observe LLM inference platforms that actually hold up under production load — with live support when you break something.
No passive watching. Every week you ship a piece of a real inference platform — and break it on purpose.
Get your task with architecture diagrams, resource budgets and success SLOs. Example: "Serve a 70B model on two A100s with vLLM and burst autoscaling."
Provision GPU node pools, wire the device plugin, debug OOMs and cold starts. Hit a wall? Debug live with your mentor on Slack/Discord.
Live trace review: p99 latency, tokens per second, cost per request. The mentor tears your platform apart — then shows you how to rebuild it better.
Build approved? Move to the next, harder platform. Every week stacks on the last one — by week 4 you own the whole stack.
This isn't a DevOps course with AI buzzwords. It's the infrastructure discipline behind production LLMs.
Serve models with vLLM, KServe and Triton. Continuous batching, quantization, and GPU autoscaling driven by real queue depth with KEDA.
Run local and private models with Ollama and vLLM. Slice GPUs with MIG, build fine-tune pipelines, and manage a model registry with versioned rollouts.
Trace every prompt, token and tool call with OpenTelemetry GenAI semantics and Langfuse. Cost per request, eval-driven regressions, dashboards and alerts.
NVIDIA device plugin, MIG slicing, bin-packing and spot/ODCR strategy. Canary model rollouts, multi-tenant isolation, and cost governance that keeps the bill sane.
Twelve days. One platform. Here's how we break it into tasks you can actually finish.
What you'll have: a multi-model LLM inference platform on Kubernetes with GPU autoscaling, full GenAI tracing, cost-per-token dashboards and canary rollouts. The exact thing AI-platform teams pay for.
Everything you're wondering before you commit 4 weeks to this.
DevOps courses teach pipelines. We teach platforms that serve models. When your inference queue backs up at 2 AM, your GPU pod OOMs, or your token bill spikes — that's what we train you to fix, live, with a mentor beside you.
No. You won't train models from scratch — you'll engineer the infrastructure they run on. If you understand Linux, Docker and basic Kubernetes, you're ready. We teach serving, scaling and observing LLMs end to end.
No. We use cloud GPUs — spot and on-demand — and teach you to tear everything down daily. Budget roughly ₹4,000–6,000 for compute during the cohort; cost governance is literally part of the curriculum.
No one can guarantee jobs, and we won't pretend otherwise. What we give you: portfolio platforms companies actually hire for, interview prep focused on AI-platform roles, and resume review. The rest is your execution.
Every Friday's platform review catches gaps before they compound. Add recorded sessions, weekend doubt-clearing and 2 h/week of 1:1 office hours — falling behind is hard if you show up.
Because every build gets a real code review and every Friday trace review is personal. Ten students means your platform gets the attention it deserves. We'd rather have 10 serious builders than 50 half-committed ones.
Next cohort starts soon — only 10 seats. Book a free 30-minute platform engineering assessment.
Complete 80% of the builds and don't see value? Full refund, no questions asked. We've never had a refund request — but we stand behind the training.
No sales pitch. We'll honestly assess whether this is right for you — and tell you if it isn't.
Our team reviews your background and goals
Pick a time for your 30-min assessment
We walk through sample platforms and answer everything
If it's a fit, join the next cohort and start shipping