AI Platform Engineer Path
Run the shared AI infrastructure every team builds on
Visual Roadmap
9 stages · click any stage to jump
Infrastructure Foundations
Containers, deployment, data stores and …
Models & Model Choice
What models are, what they cost, and whe…
Serving Models at Scale
Inference servers, batching, quantisatio…
Gateways & Routing
One secure entry point for every model, …
Cost Management
Budgets, caching, model choice and the c…
Observability
Tracing, metrics and logs for every mode…
Security & Guardrails
Platform-level defences: injection, data…
Evaluation & Release Pipelines
Versioned prompts and models, evaluation…
Capstone
Build a small AI platform that two teams…
Overview
Build and operate the platform behind production AI: model serving, LLM gateways with routing and budgets, cost and observability, security guardrails, and evaluation-gated release pipelines.
Learning outcomes
- Serve hosted and self-hosted models with known latency, throughput and cost
- Run an LLM gateway with routing, fallbacks, budgets and cost attribution
- Make every AI request observable, secure and attributable
- Release prompt and model changes with evaluation gates, shadow traffic and canaries
- Give product teams a paved road for building AI features safely
Stages
Containers, deployment, data stores and secrets: the base every AI platform runs on.
What models are, what they cost, and when to use hosted versus open models.
Inference servers, batching, quantisation, autoscaling and the economics of self-hosting.
AI Platform EngineeringOne secure entry point for every model, with routing, fallbacks, budgets and attribution.
AI Platform EngineeringBudgets, caching, model choice and the cost of reliability.
Tracing, metrics and logs for every model call and agent step.
Platform-level defences: injection, data leakage, content safety and access.
Versioned prompts and models, evaluation gates, shadow traffic, canaries and rollback.
AI Platform EngineeringBuild a small AI platform that two teams can use.