Title: Lecture — Introduction to AI Infrastructure
Course: AIINFRA 100: AI Systems & Development Environments > Section: Week 01 — Introduction to AI Infrastructure

📖 Lecture — Introduction to AI Infrastructure

What “AI infrastructure” actually means

AI infrastructure is the hardware, software, data systems, and operational practices that let AI models be built, run, and served reliably. If a model is the engine, infrastructure is the whole rest of the car plus the road, fuel, and pit crew. It's mostly invisible when it works — and very visible when it doesn't.

The layered mental model

Think of every AI system as a stack of layers:

Layer | What lives here | Examples
Serving / application | How users reach the model | APIs, inference servers, chat apps
Software | What runs the model | OS, GPU drivers/CUDA, frameworks (PyTorch), containers
Compute | The raw horsepower | CPUs, GPUs, TPUs, memory, networking
Data | What the model learns from / retrieves | storage, pipelines, datasets
Cross-cutting | Applies to every layer | security, observability, cost, governance

You will spend the certificate going up and down this stack. This course surveys all of it; later courses go deep on each.

The throughline: prototype vs. production

The defining skill of this field is turning a demo into something real.

 | Prototype | Production
Users | Just you | Many, concurrent
Runs on | Your laptop | Servers / cloud / cluster
Reliability | “Works on my machine” | Must not go down
Concerns | “Does it work at all?” | Scale, latency, cost, security, monitoring
Typical tools | Notebook, Ollama, LM Studio | Kubernetes, vLLM, observability, CI/CD

Whenever you build something this term, ask: what would it take to make this production-grade?

The AI system lifecycle

Data → obtain/train a model → package → deploy & serve → monitor → iterate. Infrastructure touches every stage: data needs storage and pipelines; training and serving need compute; deployment needs software and networking; and monitoring/cost/security wrap around all of it.

A preview of workloads (Week 2)

Two big workload types drive every infrastructure decision: training (building/adapting a model — heavy, bursty) and inference (using a model — latency-sensitive, always-on). Hold that thought; next week is all about it.

Where this certificate takes you

100 AI Systems & Development Environments (you are here) → 101 Applied Infrastructure & Containerization → 200 Production Inference Serving & GPU Orchestration → 201 Model Adaptation: Fine-Tuning & Quantization → 300 Agentic AI & the Model Context Protocol (MCP) → 301 Production RAG & LLMOps → 302 AI Security, Guardrails & Governance → 303 Capstone Project.

Key terms

Model, inference, training, GPU/VRAM, latency, throughput, container, orchestration, endpoint, token. (Defined in the glossary; you'll use all of these next week.)