Cloud Infrastructure for AI
The chapters in this module, in reading order.
| # |
Chapter |
| 00 |
Cloud Infrastructure for AI — The Five-Year-Old Version |
| 01 |
Cloud primitives and compute — VMs vs containers vs serverless |
| 02 |
Object storage and data — buckets, lifecycle, and lake patterns |
| 03 |
IAM, VPC, and security — roles, networks, and least privilege |
| 04 |
Managed databases and caches — RDS, DynamoDB, Redis, and tradeoffs |
| 05 |
GPU instances and clusters — A100, H100, multi-GPU, and NVLink |
| 06 |
Managed ML platforms — SageMaker, Vertex AI, Azure ML, and tradeoffs |
| 07 |
Secrets and config management — Vault, parameter stores, and rotation |
| 08 |
Cost controls and budgets — spot, reserved, alerts, and auto-shutdown |
| 09 |
Serverless patterns — Lambda, Cloud Functions, and workflows that fit |
| 10 |
Edge and hybrid AI — on-device inference and cloud-edge split |
| 11 |
Honest admission — multi-cloud, GPU economics, sustainability, and lock-in |