100 challenges to take you from `python --version` to shipping a FastAPI + Celery service with full observability — and the canonical 2026 RAG pipeline as the capstone.
Python is the language of data, ML, and the most-deployed backends in the world. This course teaches the 2026 stack: uv for everything, pyproject.toml as the manifest, ruff + mypy as the gate, FastAPI + SQLAlchemy 2.0 async + Alembic + Celery as the production layer, and structlog + OTel + Docker for ops. Module 1 is fully authored and free; Modules 2–10 are being authored and currently land as stubs. The capstone is `docs.ai`, a RAG ingestion pipeline that mirrors what every Python backend in 2026 is being asked to build.
Built by Lakshya Kumar
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Sign in to applyComplete all modules, then submit the required number of capstone projects. Each must earn a passing rating from an admin reviewer.
Build `docs.ai`, a document ingestion + retrieval service in Python. Required: FastAPI for multipart upload (PDFs/Markdown), Celery worker that chunks (semantic chunking), embeds (OpenAI or local), and writes to pgvector. SQLAlchemy 2.0 async + Alembic for state. A `/query` endpoint that does vector search + reranking and returns answers with citations. structlog JSON logs, OTel traces, Dockerized via multi-stage `uv` build. Submit: repo URL, a Loom of upload-to-query flow on a 100-page PDF, and an eval showing query p95 < 500 ms and citation accuracy ≥ 90% on a labeled set you create.
I'm considering Capstok's "Python: From Zero to Production Service" course. It covers modern Python toolchain (uv, pyproject, ruff, mypy), types + Pydantic, async, FastAPI, SQLAlchemy 2.0 async, Celery workers, and a production stack with structlog + OTel + Docker. Capstone is a RAG ingestion service. Context: 1. My current Python level: [e.g. "scripts only", "ML/notebook", "Django dev", "never"] 2. The closest I've come to a Python backend service: [e.g. "Flask tutorial", "Django at work", "never"] 3. What I want at the end: [e.g. "ship a Python backend", "do RAG well", "interview-ready"] Answer: - Which 2 modules will pay back fastest in my next 3 months? - A real bug I'll ship in my first FastAPI service that this course catches. - Is 50 hours worth it for me, or should I learn TS instead? Honest pick. - What this course will NOT teach (Django, Pandas, ML training)?
The book to read after Module 3 — middle-experience Python.