In this hands-on course, you’ll learn how to move computer vision models from notebooks to the real world. You’ll build an end-to-end inference pipeline, package it into a reproducible API, and evaluate its performance using precision, recall, and mean Average Precision (mAP). You’ll also practice diagnosing errors, segmenting results by condition, and communicating insights like a professional MLOps engineer. By the end, you’ll be ready to deploy, evaluate, and iteratively improve vision models that teams can trust.
What you'll learn
build an end-to-end inference pipeline
package models into a reproducible API
evaluate model performance using precision, recall, and mean Average Precision
diagnose errors in model predictions
communicate insights related to model performance
Course objectives
enable students to deploy vision models in real-world applications
equip students with techniques for model performance evaluation
develop skills in effective communication of insights within a team