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Evaluating AI Models with Azure Inference API: A Step-by-Step Guide

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that Azure AI Model Inference API Provides a unified interface for developers to interact with a variety of built-in models deployed in Azure AI Studio. This API allows developers to generate predictions from multiple models without changing the underlying code. The API simplifies the process of integrating and switching between different models by providing a consistent set of features, allowing seamless model selection based on task requirements.

characteristic

Basic models have made significant progress, especially in natural language processing and computer vision. However, these models often excel at specific tasks and can approach the same problem differently. The Azure AI Model Inference API allows developers to:

  • Performance Improvement Choose the model that best suits your specific task.
  • Optimizing Efficiency Use smaller, faster models for simpler tasks.
  • Create complex experiences By configuring multiple models.
  • Maintaining code portability It can be applied across a variety of models without sacrificing performance or functionality.

Availability of models

The Azure AI Model Inference API is available for the following models:

  1. Serverless API endpoints:
  • Managed Inference:
  • Additionally, the API is compatible with Azure OpenAI model deployment. Models deployed after June 24, 2024 can take advantage of managed inference capabilities.

    API Features

    The API supports multiple modes, allowing developers to:

    Inference SDK Support

    The Azure AI Inference SDK provides streamlined clients in multiple languages, including Python, JavaScript, and C#, so you can easily consume predictions from your models using the Azure AI Model Inference API.

    installation

    To install Python packages, use:

    pip install azure-ai-inference
    

    Example: Creating a client to complete a chat

    Here’s a simple example of how to create a client for chat completion using Python.

    import os
    from azure.ai.inference import ChatCompletionsClient
    from azure.core.credentials import AzureKeyCredential
    
    # Create a client using an API key
    client = ChatCompletionsClient(
        endpoint=os.environ["AZUREAI_ENDPOINT_URL"],
        credential=AzureKeyCredential(os.environ["AZUREAI_ENDPOINT_KEY"]),
    )
    
    # Create a client using Azure Entra ID
    from azure.identity import DefaultAzureCredential
    
    client = ChatCompletionsClient(
        endpoint=os.environ["AZUREAI_ENDPOINT_URL"],
        credential=DefaultAzureCredential(),
    )
    

    Scalability

    The API allows developers to pass additional parameters to the model beyond the specified modality. Additional parameters Header. For example, you can pass: Safe Mode Parameters for the Mistral-Large model that are not specified in the API are:

    from azure.ai.inference.models import SystemMessage, UserMessage
    
    response = client.complete(
        messages=[
            SystemMessage(content="You are a helpful assistant."),
            UserMessage(content="How many languages are in the world?"),
        ],
        model_extras={"safe_mode": True}
    )
    
    print(response.choices[0].message.content)
    

    Handling various model features

    If the model does not support a particular parameter, the API returns an error. You can handle these cases by inspecting the response.

    import json
    from azure.ai.inference.models import SystemMessage, UserMessage, ChatCompletionsResponseFormat
    from azure.core.exceptions import HttpResponseError
    
    try:
        response = client.complete(
            messages=[
                SystemMessage(content="You are a helpful assistant."),
                UserMessage(content="How many languages are in the world?"),
            ],
            response_format={"type": ChatCompletionsResponseFormat.JSON_OBJECT}
        )
    except HttpResponseError as ex:
        if ex.status_code == 422:
            response = json.loads(ex.response._content.decode('utf-8'))
            for offending in response.get("detail", []):
                param = ".".join(offending["loc"])
                value = offending["input"]
                print(f"Model doesn't support the parameter '{param}' with value '{value}'")
        else:
            raise ex
    

    Content Safety

    The API also integrates with: Azure AI Content SafetyFilters out potentially harmful content. If a request triggers a content safety measure, the response indicates this so developers can handle it appropriately.

    Get started

    To use the Azure AI Model Inference API, simply deploy a supported model to a Serverless API endpoint or Managed Online Endpoints and use the provided code to make predictions.

    Model Swapping Demo

    Here’s an example of how easy it is to change the model in a Python solution while maintaining consistency in your code.

    import os
    from azure.ai.inference import ChatCompletionsClient
    from azure.core.credentials import AzureKeyCredential
    
    # Example function to swap models
    def swap_model(endpoint_url, api_key):
        client = ChatCompletionsClient(
            endpoint=endpoint_url,
            credential=AzureKeyCredential(api_key),
        )
        return client
    
    # Swapping between two models for evaluation
    model_1 = swap_model(os.environ["MODEL1_ENDPOINT"], os.environ["MODEL1_KEY"])
    model_2 = swap_model(os.environ["MODEL2_ENDPOINT"], os.environ["MODEL2_KEY"])
    
    response_1 = model_1.complete(messages=[UserMessage(content="What's the weather today?")])
    response_2 = model_2.complete(messages=[UserMessage(content="What's the weather today?")])
    
    # Compare the results from the two models
    print("Model 1 Response:", response_1.choices[0].message.content)
    print("Model 2 Response:", response_2.choices[0].message.content)
    

    Compare model output using Azure Inference API

    The Azure Inference API provides a convenient way to evaluate the effectiveness of different models and compare their outputs. The API lets you easily switch between models and test their performance on a given input prompt.

    Here’s an example of how to compare the output of two models using the Azure Inference API.

    import os
    from azure.ai.inference import ChatCompletionsClient
    from azure.core.credentials import AzureKeyCredential
    from azure.ai.inference.models import UserMessage
    
    # Set up the models
    model_1_endpoint = "https://your-resource-name.cognitiveservices.azure.com/"
    model_1_key = "your-model-1-key"
    model_2_endpoint = "https://your-resource-name.cognitiveservices.azure.com/"
    model_2_key = "your-model-2-key"
    
    # Example function to swap models
    def swap_model(endpoint_url, api_key):
        client = ChatCompletionsClient(
            endpoint=endpoint_url,
            credential=AzureKeyCredential(api_key),
        )
        return client
    
    # Swapping between two models for evaluation
    model_1 = swap_model(model_1_endpoint, model_1_key)
    model_2 = swap_model(model_2_endpoint, model_2_key)
    
    # Set the model names for clarity
    model_1_name = "text-davinci-002"
    model_2_name = "text-curie-001"
    
    # Set the input prompt
    input_prompt = "What's the weather today?"
    
    # Get responses from both models
    response_1 = model_1.complete(messages=[UserMessage(content=input_prompt)], model=model_1_name)
    response_2 = model_2.complete(messages=[UserMessage(content=input_prompt)], model=model_2_name)
    
    # Compare the results from the two models
    print(f"Model 1 ({model_1_name}) Response:", response_1.choices[0].message.content)
    print(f"Model 2 ({model_2_name}) Response:", response_2.choices[0].message.content)

    Compare and contrast:

    Both models respond to input prompts, but differ in style and level of detail.

    • text-davinci-002 (Model 1) provides conversational, friendly responses that recognize the user’s question and offer suggestions on how to find the answer. The responses are longer, more elaborate, and have a more personal touch.
    • text-curie-001 (Model 2) provides a more concise and direct response, simply stating that it does not know the current weather and providing suggestions on how to find out the weather. The response is shorter and more to the point.

    In general, text-davinci-002 is known for its ability to generate more creative and conversational responses, while text-curie-001 is known for its ability to provide more accurate and informative responses. This is reflected in their responses to this input prompt.

    conclusion:

    Using the Azure Inference API is a great way to evaluate the effectiveness of your models and compare their outputs quickly and easily. By testing your models against a given input prompt, you can gain valuable insight into the strengths and weaknesses of each model and make informed decisions about which model to use for a particular use case.

    Our Exploration Sample And read on API Reference Documentation Click here to get started.





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