4. Model Card

The model card for a selected model provides all the necessary information to help you understand its capabilities, pricing, quality and more. And, it provides the starting point for deploying the model to explore it interactively.

4.1 Overview

1. Click the gpt-4o-mini result to navigate to the model card in Azure.

   • You should see this - note the links to pricing and estimated cost.

     FIGURE: (click to expand) Model Card Overview (Details tab - top)

     

   • Scroll down. You see model provider details on tasks and benchmarks of relevance.

     FIGURE: (click to expand) Model Card Overview (Details tab - bottom)

     

4.2 Benchmarks

1. Click the Benchmarks tab in the model card.

   • The top half of the page provides this view. Clicking Compare with other models takes you to the Benchmarks view from earlier, but with this model as main focus (and other example models for comparison).

     FIGURE: (click to expand) Benchmarks tab - compare other models

     

   • Scroll down. You should see options to try evaluating the model with your own data.

     FIGURE: (click to expand) Benchmarks tab - try with your own data

     

4.3 Deployment

1. Click the Code samples tab in the model card. You should see code snippets for using this model programmatically with the Azure AI Inference SDK, for various languages. But what if you want to explore this model in a playground in the portal?

   FIGURE: (click to expand) Code Samples tab - pick your language

   

2. Click the Details tab to get back to the overview (guest mode). Note the Create a subscription to deploy button indicating we need to log into Azure before we can proceed. Let's do that next.

   FIGURE: (click to expand) Model Card Overview (Guest Mode)

   

3. Logging in gives us a Deploy button as shown. Clicking that now gives you the choice of deploying the model to an existing project, or creating a new project for this purpose.

   FIGURE: (click to expand) Model Card Overview (Authenticated)

   

In the next lab we'll continue from this point to explore Project setup and model deployment. But first, a quick note on data, privacy, and security considerations when working with models in the Azure AI model catalog.