Phase 3: Generative Vision Models · ~120 minutes · Python

Build a Complete Vision Pipeline — Capstone

A production vision system is a chain of models and rules stitched with data contracts. The pieces are already in this phase; the capstone wires them together end-to-end.

Hiring signal: Understanding of build a complete vision pipeline — capstone internals

What you will learn

Design a production vision pipeline that detects objects, classifies them, and emits structured JSON — with every failure path handled
Plug a detector (Mask R-CNN or YOLO), a classifier (ConvNeXt-Tiny), and a data contract (Pydantic) into one service
Benchmark the end-to-end pipeline and identify the first bottleneck (usually preprocessing, then the detector)
Ship a minimal FastAPI service that accepts an image upload, runs the pipeline, and returns detections with classifications

Introduction

Type: Build Languages: Python Prerequisites: Phase 4 Lessons 01-15 Time: ~120 minutes

Learning Objectives

Design a production vision pipeline that detects objects, classifies them, and emits structured JSON — with every failure path handled
Plug a detector (Mask R-CNN or YOLO), a classifier (ConvNeXt-Tiny), and a data contract (Pydantic) into one service
Benchmark the end-to-end pipeline and identify the first bottleneck (usually preprocessing, then the detector)
Ship a minimal FastAPI service that accepts an image upload, runs the pipeline, and returns detections with classifications

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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