Azure OpenAI Service Intermediate

Azure OpenAI Service provides enterprise access to OpenAI's most powerful models — GPT-4, GPT-4o, DALL-E 3, and Whisper — with Azure's security, compliance, and regional availability. Your data is not used to train models, and all traffic stays within your Azure subscription.

Available Models

Model Type Use Cases
GPT-4o Multi-modal (text + vision) Chat, analysis, reasoning, image understanding
GPT-4 Text generation Complex reasoning, coding, content creation
GPT-3.5 Turbo Text generation Fast chat, summarization, classification
DALL-E 3 Image generation Creating images from text descriptions
Whisper Speech-to-text Audio transcription, translation
Text Embedding Embeddings Semantic search, RAG, clustering

Creating a Deployment

Terminal 
# Create an Azure OpenAI resource
az cognitiveservices account create \
  --name my-openai-resource \
  --resource-group ai-school-rg \
  --kind OpenAI \
  --sku S0 \
  --location eastus

# Deploy a GPT-4o model
az cognitiveservices account deployment create \
  --name my-openai-resource \
  --resource-group ai-school-rg \
  --deployment-name gpt-4o-deployment \
  --model-name gpt-4o \
  --model-version "2024-05-13" \
  --model-format OpenAI \
  --sku-capacity 10 \
  --sku-name Standard

Using the Chat Completions API

Python 
from openai import AzureOpenAI

client = AzureOpenAI(
    api_key="YOUR_API_KEY",
    api_version="2024-02-01",
    azure_endpoint="https://my-openai-resource.openai.azure.com/"
)

# Chat completion
response = client.chat.completions.create(
    model="gpt-4o-deployment",  # Your deployment name
    messages=[
        {"role": "system", "content": "You are a helpful AI assistant."},
        {"role": "user", "content": "Explain Azure OpenAI in 3 sentences."}
    ],
    temperature=0.7,
    max_tokens=500
)

print(response.choices[0].message.content)

Image Generation with DALL-E 3

Python 
# Generate an image
result = client.images.generate(
    model="dall-e-3",
    prompt="A futuristic city powered by AI, digital art style",
    size="1024x1024",
    quality="hd",
    n=1
)

image_url = result.data[0].url
print(f"Image URL: {image_url}")

Speech-to-Text with Whisper

Python 
# Transcribe audio
with open("audio.mp3", "rb") as audio_file:
    transcription = client.audio.transcriptions.create(
        model="whisper-deployment",
        file=audio_file,
        language="en"
    )

print(transcription.text)

Enterprise Features

  • Content Filtering: Built-in content safety filters for hate speech, violence, sexual content, and self-harm with configurable severity levels
  • Private Endpoints: Access Azure OpenAI over private network connections within your VNet
  • Managed Identity: Authenticate without API keys using Azure AD managed identities
  • Rate Limiting: Configurable tokens-per-minute (TPM) and requests-per-minute (RPM) limits per deployment
  • Data Residency: Choose from multiple Azure regions to keep data within specific geographies
Azure OpenAI vs OpenAI API: The APIs are nearly identical — you use the same openai Python library. The key differences are: Azure requires a deployment name instead of a model name, uses Azure-specific authentication, and adds enterprise features like content filtering and private networking.

Models Deployed!

You can now use GPT-4, DALL-E, and Whisper through Azure. Next, explore Azure Machine Learning Studio for building custom ML models.

Next: ML Studio →
← Setup ML Studio →