Azure OpenAI Service

Azure OpenAI Service provides REST API access to OpenAI's powerful language models, including GPT-4, GPT-3.5-Turbo, Codex, and DALL-E, with enterprise-grade security and compliance.

Available Models

GPT Models

GPT-4

GPT-4: Most capable model for complex tasks
GPT-4 Turbo: Optimized for speed and cost
GPT-4 Vision: Multimodal model that can process images

Capabilities: - Advanced reasoning and problem-solving - Code generation and debugging - Creative writing and content creation - Complex question answering

GPT-3.5-Turbo

Optimized for chat and conversational AI
Cost-effective for many use cases
Fast response times

Codex Models

code-davinci-002: Code generation and completion
code-cushman-001: Lighter version for simpler tasks

Embedding Models

text-embedding-ada-002: High-quality text embeddings
text-similarity-*: Specialized for similarity tasks

DALL-E

DALL-E 2: Generate images from natural language descriptions

Key Features

Enterprise Security

Private networking with VNet support
Customer-managed keys
Azure Private Link
Role-based access control (RBAC)

Content Filtering

Built-in content filtering
Customizable content policies
Harmful content detection
Bias mitigation

Fine-tuning

Customize models with your data
Improve performance for specific tasks
Maintain data privacy during training

Responsible AI

Content filtering and safety systems
Transparency and explainability tools
Bias detection and mitigation
Human oversight capabilities

Getting Started

1. Request Access

Apply for access to Azure OpenAI Service (approval required).

2. Create Resource

az cognitiveservices account create \
  --name myopenai \
  --resource-group myresourcegroup \
  --kind OpenAI \
  --sku S0 \
  --location eastus

3. Deploy Model

from openai import AzureOpenAI

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

deployment_name = "gpt-4"

Common Use Cases

Conversational AI

response = client.chat.completions.create(
    model=deployment_name,
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain quantum computing"}
    ],
    max_tokens=150,
    temperature=0.7
)

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

Code Generation

response = client.completions.create(
    model="code-davinci-002",
    prompt="# Python function to calculate fibonacci sequence\ndef fibonacci(",
    max_tokens=100,
    temperature=0
)

Text Summarization

response = client.chat.completions.create(
    model=deployment_name,
    messages=[
        {"role": "system", "content": "Summarize the following text in 50 words."},
        {"role": "user", "content": "Your long text here..."}
    ],
    max_tokens=80
)

Content Generation

response = client.chat.completions.create(
    model=deployment_name,
    messages=[
        {"role": "system", "content": "You are a creative writing assistant."},
        {"role": "user", "content": "Write a short story about AI"}
    ],
    max_tokens=500,
    temperature=0.8
)

Best Practices

Prompt Engineering

Be Specific

Provide clear, specific instructions and examples in your prompts.

Good Prompt

Analyze the following customer review and classify the sentiment as positive, negative, or neutral. Also extract key topics mentioned.

Review: "The product arrived quickly but the quality was disappointing."

Sentiment:
Topics:

Token Management

Monitor token usage for cost optimization
Use appropriate max_tokens settings
Consider model choice based on task complexity

Security

Store API keys securely (Azure Key Vault)
Implement proper authentication
Use managed identities when possible
Enable logging and monitoring

Content Filtering

Configure content filters appropriately
Test with various inputs
Implement fallback mechanisms
Monitor filtered content

Performance Optimization

Batch requests when possible
Use streaming for real-time applications
Implement caching for repeated queries
Choose appropriate temperature settings

Pricing and Quotas

Pricing Model

Pay-per-token usage
Different rates for different models
Separate pricing for fine-tuning

Quota Management

Request quota increases as needed
Monitor usage against quotas
Implement rate limiting in applications

Cost Optimization

Choose the right model for the task
Optimize prompt length
Use caching for repeated requests
Monitor and analyze usage patterns

Monitoring and Logging

Azure Monitor Integration

Track API calls and latency
Monitor error rates
Set up alerts for issues

Application Insights

Detailed telemetry and performance data
Custom metrics and events
End-to-end transaction tracking

Logging Best Practices

Log prompts and responses (consider privacy)
Track token usage
Monitor content filtering events
Implement audit trails