Cloud Computing for AI Systems from First PrinciplesPost 19 of 19 · 19 of 19 written
  1. How to use this guide
  2. 01The core mental model
  3. 02The physical layer
  4. 03Virtualization: how one machine becomes many
  5. 04Compute for AI: GPUs and accelerators
  6. 05Back-of-envelope math for AI workloads
  7. 06Storage and data
  8. 07Networking
  9. 08Identity, security and compliance
  10. 09Orchestration: running fleets, not machines
  11. 10Infrastructure as code and delivery
  12. 11Distributed training in the cloud
  13. 12Inference and serving
  14. 13Reliability and observability
  15. 14Cloud economics
  16. 15The provider landscape
  17. 16Fluency toolkit: the questions to ask
  18. 17Hands-on learning path
  19. 18Glossary
  20. 19Resources worth your time

19. Resources worth your time

Cloud · 19 of 191 min readOct 2026

A short list, chosen because each one teaches from first principles rather than product names. All are easy to find by title; most are free.

Systems foundations

  • Designing Data-Intensive Applications (Martin Kleppmann) - storage, replication, consistency and streams; the best single book behind Parts 6 and 13.
  • Site Reliability Engineering and The Site Reliability Workbook (Google, free online) - SLOs, error budgets, incidents.
  • Latency Numbers Every Programmer Should Know - the classic table behind Part 2.
  • Kubernetes the Hard Way (Kelsey Hightower, GitHub) - builds a cluster by hand so nothing in Part 9 stays magic.
  • AWS Well-Architected Framework, with its machine learning and generative AI lenses - a structured design-review checklist.

AI systems and hardware

  • Making Deep Learning Go Brrrr From First Principles (Horace He, blog post) - compute, memory bandwidth and overhead; the perfect companion to Part 4.
  • How to Scale Your Model (Google DeepMind, free online) - rooflines, parallelism and inference math; TPU-flavored but universal.
  • The Ultra-Scale Playbook (Hugging Face, free) - training LLMs on GPU clusters, step by step, with measurements.
  • Machine Learning Engineering Open Book (Stas Bekman, GitHub) - practical notes on GPUs, networks, storage, debugging and failures at scale.
  • AI Engineering and Designing Machine Learning Systems (Chip Huyen) - building applications and production ML systems.
  • Papers: “Efficient Memory Management for Large Language Model Serving with PagedAttention” (the vLLM paper), the FlashAttention papers, and the infrastructure sections of “The Llama 3 Herd of Models” and the DeepSeek-V3 technical report.

Staying current

  • The vLLM, SGLang, PyTorch and NVIDIA technical blogs, plus release notes of the tools you use.
  • Provider pricing pages and “what’s new” feeds for the cloud you choose.
  • MLPerf training and inference results, read together with their system configurations.