Learn to build a scalable, event-driven personal data pipeline using FastAPI, Kafka, and PostgreSQL — a portfolio project that signals senior-level systems thinking.
A step‑by‑step guide to building a user‑safety service with FastAPI, Postgres, Celery and Docker that you can run, test, and extend for senior‑level impact.
Ship a real webhook delivery service with idempotency, retries, and a dead-letter queue — a portfolio project that signals genuine distributed-systems skill to hiring managers.
How to build FastAPI services that survive contact with GPU workloads, long inference calls, and real users. Patterns, anti-patterns, and production code.
FastAPI’s speed, async capabilities, and automatic API documentation make it ideal for building production-grade APIs serving Large Language Models (LLMs). This guide details best practices for deploying scalable, secure FastAPI applications handling LLM inference, streaming responses, and high-throughput requests.[1][3][5] LLM APIs often face unique challenges: high memory usage, long inference times, streaming outputs, and massive payloads. We’ll cover project structure, async optimization, security, deployment, and LLM-specific patterns like token streaming and caching. ...