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Production backends for AI: advanced Git and teamwork, API design, async clients, FastAPI architecture, SQLAlchemy and Alembic, auth, Redis, testing, ML and LLM services, security, Docker, CI/CD and deployment — every example really run.
Twenty-two pages for after the beginner tutorial. You learn Git the way teams use it (rebase, reflog, reviews, rulesets, Git for data and models), design robust HTTP APIs (idempotency, caching, OAuth, pagination, retries, webhooks, streaming), write async API clients, and structure FastAPI for production with dependencies, a real database and migrations, authentication, Redis caching and rate limits, and a serious test suite.
Then you build AI services — a model-serving endpoint and an LLM backend with streaming, usage limits and cost logging — secure them against the OWASP API Top 10, package them with Docker and Compose, automate CI/CD with GitHub Actions and deploy. It ends with a practice and interview lab and a capstone project. English and Bangla.
6 chapters · 609 min total
Watch it taught, then check what stuck.