From raw data to production inference. Six stages, two tracks.
Classical ML (features → model) or LLM / GenAI (prompts → context) — same skeleton, different muscle. This poster maps the full pipeline for both.
Vibe edition
Clean layout covering data pipeline, features & prompts, training, evaluation, deployment, and MLOps monitoring. Plus the GPU infrastructure decision guide — when to use API, when to self-host, quantization, and inference engines.
What's on it
7 sections — data pipeline (batch vs stream), feature engineering + prompt engineering, training (PyTorch to Claude/GPT), evaluation (classification to LLM-as-judge), deployment (batch to serverless), MLOps, and GPU infrastructure.
The details
18" × 24" matte giclée print, 300 DPI. Dark theme.
Companion products: AI Agent Blueprint + Python AI Libraries desk mats. quux.shop