Week 1: Backend Infrastructure Ramp
A first weekly learning log for backend, deployment, security, observability, and AI infrastructure readiness.
- Status
- published
- Visibility
- public
- Category
- Learning Log
- Difficulty
- beginner
- Published
- Jun 28, 2026
- Updated
- Jun 28, 2026
Focus
This week is about turning broad infrastructure experience into a sharper role-specific map:
- FastAPI production service shape.
- Cloudflare Pages deployment workflow.
- AI API reliability patterns.
- GPU inference vocabulary.
- Secrets, IAM, RBAC, and observability basics.
Notes
The useful pattern is not to memorize every provider setting. The useful pattern is to know what questions to ask before a service becomes production-critical:
- What owns the secret?
- What happens when an external model API slows down?
- What is the fastest safe rollback?
- Which logs would explain a failed request?
- Which costs scale with traffic, retries, or media size?
Small Wins
- Created the initial knowledge base structure.
- Added role relevance to every note.
- Added a public/private publishing policy.
Next
- Write
GCP for Backend Engineers. - Draft
RunPod GPU Inference Notes. - Convert this log into a reusable weekly template.
Source Links
Related Notes
FastAPI Production Checklist
A compact checklist for taking a FastAPI service from useful prototype to production-ready backend.
Cloudflare Pages Deployment Runbook
A deployment checklist for publishing the knowledge base to Cloudflare Pages and mapping notes.bianrui.net.
Backend and AI Infrastructure Roadmap
A role-readiness roadmap for backend, cloud, data, AI API, and production infrastructure skills.
Why I'm Building an AI Infrastructure Learning OS
A personal operating system for turning backend and AI infrastructure learning into durable, searchable engineering knowledge.
API Design for Backend Services
A compact mental model for designing reliable, boring, useful APIs.
Backlinks
Why I'm Building an AI Infrastructure Learning OS
A personal operating system for turning backend and AI infrastructure learning into durable, searchable engineering knowledge.