Enterprise RAG infrastructure with Kubernetes and Ray
Ship a RAG service that survives real production traffic on Kubernetes.
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Shipping AI means deploying Python services that talk to GPUs, manage API budgets, and don't fall over when a model rate-limits. These courses cover the operational side engineers need to own: Dockerizing FastAPI services, GitHub Actions pipelines, observability stacks, and the 12-factor discipline that keeps all of it maintainable.
Curated by Param Harrison
You won't find a Kubernetes deep-dive here. The focus is enough DevOps to ship. That means the minimum viable pipeline a solo engineer or small team can actually keep running.
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Common questions
Whichever one you already use. The patterns (Dockerfile, CI, secrets, logs) transfer across AWS, GCP, and Fly/Render. These courses stay vendor-agnostic where possible and call out the differences that matter.
Probably not. Most AI apps run fine on managed services (Fly, Railway, Render, App Runner). Pick Kubernetes when you hit specific scale or team constraints, not before.
Directly. Every FastAPI course here has a Docker and CI counterpart. Do them in order and you’ll have a service that builds, deploys, and stays deployed.
No. SRE work dives deeper into capacity, reliability math, and incident response. This track teaches the DevOps practices every application engineer should own. Good SREs need that foundation plus broader systems work.