Skip to content
Back to Blog
1 min read

Azure AI Studio Updates at Build 2024

I wrote “Azure AI Studio Updates at Build 2024” to share practical, production-minded guidance on this topic.

What’s New in Azure AI Studio

1. Unified Experience

Azure AI Studio now provides a single pane of glass for:

  • Model catalog browsing
  • Prompt engineering
  • Fine-tuning
  • Evaluation
  • Deployment
  • Monitoring

2. Model Catalog Expansion

Available Models:
├── OpenAI (GPT-4o, GPT-4, GPT-3.5)
├── Microsoft (Phi-3 family)
├── Meta (Llama 3)
├── Mistral (Mistral, Mixtral)
├── Cohere (Command R+)
└── Stability AI (SDXL)

Getting Started with AI Studio

Project Setup

from azure.ai.projects import AIProjectClient
from azure.identity import DefaultAzureCredential

# Connect to AI Studio project
credential = DefaultAzureCredential()
client = AIProjectClient(
    credential=credential,
    subscription_id="your-subscription-id",
    resource_group_name="your-resource-group",
    project_name="your-ai-project"
)

# List available models
models = client.models.list()
for model in models:
    print(f"{model.name}: {model.version}")

Model Deployment

from azure.ai.projects.models import ModelDeployment

# Deploy a model
deployment = client.deployments.create(
    name="gpt4o-production",
    model="gpt-4o",
    sku="Standard",
    capacity=10  # TPM in thousands
)

print(f"Deployment created: {deployment.name}")
print(f"Endpoint: {deployment.endpoint}")

Prompt Flow Updates

Visual Flow Builder

# prompt_flow.yaml
$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
inputs:
  query:
    type: string
    description: User question
  context:
    type: string
    description: Retrieved documents

nodes:
  - name: search
    type: python
    source:
      type: code
      path: search.py
    inputs:
      query: ${inputs.query}

  - name: generate
    type: llm
    source:
      type: code
      path: generate.prompty
    inputs:
      context: ${search.output}
      query: ${inputs.query}

outputs:
  answer:
    type: string
    reference: ${generate.output}

Prompty Files

# generate.prompty\n\n## Takeaways\n\n*Add a concise, personal takeaway and recommended next steps here.*\n
Michael John Peña

Michael John Peña

Senior Data Engineer based in Sydney. Writing about data, cloud, and technology.