A practical directory of the frameworks, infrastructure, data systems, and inference engines behind production AI. Every entry opens a focused implementation guide.
How to use this library. Read the mental model and decision guide before copying commands. Then run the focused validation lab with your own workload and keep the production checklist beside the deployment review.
Some tools appear in more than one category because they span layers—for example, a serving framework can also be a workload runtime. Those entries intentionally point to one canonical guide.
The application layer — frameworks, tools, memory, and retrieval that turn a model into an agent.
The platform layer — what schedules, serves, stores, and secures AI workloads on Kubernetes.
Where the data lives and how it's wrangled — lakehouse formats and the science toolkit.
Serving the model — the engines and runtimes that turn weights into tokens.