Ahmed Abdelwahed

Ahmed Abdelwahed

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ahmed@abdelwahed.me

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Azure AI – Hands-On Guide

Azure AI | Hands-On Guide — Ahmed Abdelwahed
Microsoft Azure Azure OpenAI Computer Vision Version 25.04 Hands-On Guide

Azure AI
Hands-On

A practical, screenshot-driven guide covering Azure AI services end-to-end — from training custom image classifiers and deploying object detection apps to working with Azure OpenAI, DALL·E, and real-time audio models.

Ahmed Abdelwahed Azure AI Practitioner Guide

About This Guide

This guide takes a hands-on, do-it-yourself approach to Azure AI. Rather than theoretical coverage, every section walks through a real lab you can replicate — creating Custom Vision projects, training image classifiers, building Streamlit web apps, deploying Azure OpenAI with your own data, and experimenting with DALL·E image generation and real-time audio. All screenshots are taken from live Azure environments, so what you see in the guide is exactly what you’ll see on screen.

What You’ll Build

🐾

Image Classification

Train a Custom Vision model to classify cats, dogs, and lions — then retrain with big cat datasets from Kaggle.

🍉

Object Detection

Build a fruit detector that draws bounding boxes around objects in images using Azure Custom Vision.

🌐

Streamlit Web Apps

Deploy prediction apps on Linux VMs and Streamlit Cloud — with webcam support via GitHub integration.

📷

Computer Vision (OCR)

Set up Azure Computer Vision and build a Flask OCR web app to extract text from uploaded images.

🤖

Azure OpenAI + RAG

Deploy GPT-4o in Azure AI Foundry, connect your own documents via AI Search, and publish a chat web app.

🎨

DALL·E & Audio

Generate images from text prompts using DALL·E 3, and experiment with real-time audio using GPT-4o audio models.

Lab Contents

01

Image Classification with Custom Vision

Create resource, upload images, tag and train — achieve 100% precision & recall in Iteration 1.

02

Classify Big Cats

Download Kaggle big cat dataset (cheetah, leopard, lion, tiger) and train a multiclass classifier.

03

Prediction via Python API

Publish trained iterations, obtain prediction URL & key, and call the API from Python code.

04

BigCat Streamlit App on Linux

Install Streamlit on Rocky Linux, build an upload-and-predict app, run on port 8501.

05

Object Detection — Fruit Detector

Tag bounding boxes for 7 fruit classes (15+ images each), train, publish, and test Quick Test.

06

Object Detection App + Webcam

Deploy fruit detector app on Streamlit Cloud via GitHub with live webcam and snapshot support.

07

Computer Vision & OCR Flask App

Create Computer Vision resource, build Flask app on Ubuntu VM, expose on Azure port 5000.

08

Azure OpenAI Chat Playground

Create Azure OpenAI resource, deploy GPT-4.1, explore Chat Playground in Azure AI Foundry.

09

RAG — Add Your Own Data

Connect Azure AI Search, upload documents, configure semantic chunking, and query your data.

10

Deploy OpenAI as a Web App

One-click deploy from AI Foundry to Azure App Service with Entra ID auth and CosmosDB chat history.

11

Real-Time Audio — GPT-4o

Deploy gpt-4o-realtime-preview in East US 2, enable microphone, and hold live voice conversations.

12

Image Generation — DALL·E 3

Deploy DALL·E 3 in the Images Playground and generate images from text prompts.

Tech Stack

Azure Custom Vision Azure Computer Vision Azure OpenAI Azure AI Foundry Azure AI Search Azure App Service Python Streamlit Flask Pillow / PIL DALL·E 3 GPT-4o Audio Babbage-002 GPT-4.1

Key Concepts

🔁

Iterative Model Training

Each time you retrain a Custom Vision model, a new iteration is created. Iterations let you compare model versions and roll back — making it safe to add new tags and improve accuracy without losing your previous working model.

🗂️

Classification vs. Object Detection

Image classification labels the whole image. Object detection goes further — it identifies what objects are present and where they are, returning bounding box coordinates for each detected object.

📎

RAG with Azure AI Search

Retrieval-Augmented Generation grounds GPT responses in your own documents. Azure AI Search indexes your data into chunks; when a user asks a question, the most relevant chunks are retrieved and passed to GPT as context before generating the answer.

Who Is This For

🏗
AI-900 / AI-102 Students

Hands-on companion material for Azure AI Fundamentals and AI Engineer certifications.

📚
MCT Trainers

Live lab reference for delivering Azure AI courses to enterprise and government audiences.

🤖
AI / ML Developers

Quickly spin up real Vision and OpenAI workloads with working Python code included.

🔐
Solutions Architects

Evaluate Azure AI service options for computer vision, NLP, and generative AI use cases.

⚙️
DevOps Engineers

Learn how to deploy AI-powered apps to Azure App Service and Streamlit Cloud via GitHub.

🎓
Cloud Beginners

Step-by-step screenshots make every lab followable with zero prior AI experience required.

Guide Details

Version25.04
·
PlatformMicrosoft Azure
·
FormatScreenshot-Driven Labs
·
AuthorAhmed Abdelwahed

Download the Hands-On Guide

12 practical Azure AI labs — Custom Vision, Computer Vision, Azure OpenAI, DALL·E 3, and real-time audio. Free to download, no sign-up required.