Analyzing Faces with Azure AI

Pluralsight Hands-On Lab — Azure AI Services

Analyzing Faces with Azure AI

Pluralsight Hands-On Lab — AI & Containers

At a Glance

Platform	Pluralsight
Category	Azure AI Services
Lab Type	Guided + Challenge Mode
Environment	Azure Portal, Azure Cloud Shell (Bash), Python
Completed	2026

Overview

Azure AI Face is a cognitive service that detects and analyzes human faces in images, returning facial attributes such as age, emotion, and other characteristics. In this lab, I used a pre-provided Python command-line application to connect to an Azure AI Services resource, submit an image for facial analysis, and review the returned attributes. I configured the application with the service endpoint and API key, then ran it from Azure Cloud Shell.

What I Did

Previewed the target image hosted on GitHub to consider what facial attributes the AI service might return before running the analysis
Opened Azure Cloud Shell in Bash mode, configured it to use the existing storage account and a new file share named "faceanalysis"

Cloned the AI-900 lab repository from GitHub:

git clone https://github.com/pluralsight-cloud/AI-900-Artificial-Intelligence-Workloads-and-Considerations.git

Changed into the repository directory and installed Python dependencies:
```
pip install -r requirements.txt --user
```
Opened the Cloud Shell editor and navigated to 2-face-analysis.py, then replaced the placeholder endpoint and key values on lines 13 and 14 with the Azure AI Services resource's endpoint URL and KEY 1
Ran the application and reviewed the facial attribute output returned by the Azure AI Face service:
```
python 2-face-analysis.py
```

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