Deploy & Evaluate Vision Models Effectively

Coursera MOOC / Non-credit USD 49
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Deploy & Evaluate Vision Models Effectively

About this course

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

Skills you'll gain

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