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// AIINFRA 100 · Semester 1
Introduction to AI Systems & Infrastructure
The foundation course: AI workload, compute, data & deployment basics
An introduction to the systems and infrastructure that power modern artificial intelligence. Students learn what AI workloads are, the compute, data, and software layers they depend on, and how AI systems are deployed across cloud, on-premises, and hybrid environments. The course threads a prototype-vs-production mindset throughout and previews the security, governance, and inference-serving topics that later certificate courses cover in depth. No prior AI experience required.
01 / outcomes
Outcomes
Course objectives
- Define key AI concepts and terminology relevant to infrastructure planning.
- Identify appropriate infrastructure components (compute, data, software) for specific AI workloads.
- Evaluate cloud, on-premises, and hybrid deployment models against real requirements and constraints.
- Apply responsible-AI and governance frameworks (NIST AI RMF, EU AI Act at survey level) to infrastructure decisions.
- Configure a basic, containerized AI development environment for building and testing.
Student learning outcomes
- Define key AI concepts and terminology for infrastructure planning
- Identify infrastructure components (compute/data/software) for a given AI workload
- Evaluate cloud vs. on-prem vs. hybrid deployment models against requirements
- Apply responsible-AI/governance frameworks (NIST AI RMF, EU AI Act) to decisions
- Configure a basic containerized AI development environment
02 / schedule
16-week schedule
Wk 01
Introduction to AI Infrastructure
Frames the course's prototype-vs-production theme and orients students to AI infrastructure concepts.
Wk 02
AI Workloads & Infrastructure Requirements
Distinguishes training vs. inference workloads and the infrastructure requirements each demands.
Wk 03
Computational Resources for AI
Surveys computational resources for AI, including CPUs, GPUs, TPUs, and other accelerators.
Wk 04
AI Software Infrastructure
Covers AI software infrastructure: frameworks, CUDA, and containers underlying AI systems.
Wk 05
Data Infrastructure for AI
Examines the data infrastructure that feeds AI systems, from storage to pipelines.
Wk 06
Cloud-Based AI Infrastructure
Explores how AI infrastructure is provisioned and run on cloud platforms.
Wk 07
On-Premises AI Infrastructure
Examines on-premises approaches to hosting and running AI infrastructure.
Wk 08
Hybrid Deployment Models & Midterm
Covers hybrid cloud/on-prem deployment models; includes the course midterm exam.
Midterm · covers Wks 1–7Wk 09
Scaling AI Infrastructure
Looks at scaling AI infrastructure, with an introduction to inference serving.
Wk 10
AI Infrastructure Security
Surveys AI infrastructure security, including an overview of the OWASP LLM Top 10.
Wk 11
Monitoring, Logging & Observability
Covers monitoring, logging, and observability practices for AI infrastructure.
Wk 12
Infrastructure for the AI Development Lifecycle
Introduces infrastructure supporting the AI development lifecycle, including MLOps basics.
Wk 13
Ethical & Responsible AI Infrastructure
Applies responsible-AI governance frameworks, including the NIST AI RMF and EU AI Act, to infrastructure.
Wk 14
Cost Optimization for AI Infrastructure
Introduces cost optimization (FinOps) practices for AI infrastructure.
Wk 15
AI Infrastructure Trends & Future Directions
Discusses emerging trends and future directions in AI infrastructure.
Wk 16
Capstone Project & Course Review
Students complete a final capstone project and review the course as a whole.
Capstone03 / tools
Tools & frameworks
Local Development
Cloud Platforms
Compute & Accelerators
AI Software Stack
Governance & Security Frameworks
Operations Practices