Backend and AI Infrastructure Roadmap

Goal

Build a practical backend and AI infrastructure base that transfers across startups, platform teams, internal tools teams, and AI product teams.

Core Backend

• HTTP fundamentals: methods, status codes, headers, caching, idempotency.
• API contracts: OpenAPI, request/response models, pagination, filtering, errors.
• Python services: FastAPI, Pydantic, async I/O, dependency injection, background work.
• Data access: SQL, transactions, indexes, migrations, connection pools.
• Operational hygiene: health checks, graceful shutdown, structured logs, dashboards.

Cloud and Deployment

• Cloud Run or similar container platforms for stateless services.
• Cloud SQL or managed Postgres for relational workloads.
• Object storage for media and artifacts.
• Pub/Sub or queues for asynchronous jobs.
• Kubernetes or GKE when orchestration, scheduling, or GPU workloads justify the complexity.

AI Infrastructure

• LLM API integration with timeouts, retries, budgets, and provider failover.
• GPU inference concepts: image size, cold start, batching, VRAM, model artifacts.
• Hugging Face and model registry vocabulary.
• Creative media pipeline stages: ingest, preprocess, inference, postprocess, review, export.

Security and Reliability

• Secrets never live in source code.
• IAM and RBAC start from least privilege.
• Logs avoid private data and tokens.
• Every external dependency has a timeout and an error policy.
• Every launch has a rollback path.

Weekly Learning Loop

1. Pick one domain.
2. Write one public transferable note.
3. Add one checklist.
4. Link it to a role-readiness group.
5. Revisit it after using the concept in a real build.