Deploy & Evaluate Vision Models Effectively

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

• Move computer vision models from notebooks to production
• Build end-to-end inference pipelines and package as reproducible APIs
• Evaluate performance using precision, recall, and mean Average Precision (mAP)
• Diagnose errors and segment results by condition
• Communicate insights professionally like an MLOps engineer

• Basic familiarity with machine learning and computer vision terminology
• Readiness to practice through applied exercises or case studies