When you develop a generative AI app, you need to integrate language models into your application. To be able to use a language model, you need to deploy the model. Let’s explore how to deploy language models in the Azure AI Foundry, after first understanding why to deploy a model.
Why deploy a model?
You train a model to generate output based on some input. To get value out of your model, you need a solution that allows you to send input to the model, which the model processes, after which the output is visualized for you.
With generative AI apps, the most common type of solution is a chat application that expects a user question, which the model processes, to generate an adequate response. The response is then visualized to the user as a response to their question.
You can integrate a language model with a chat application by deploying the model to an endpoint. An endpoint is a specific URL where a deployed model or service can be accessed. Each model deployment typically has its own unique endpoint, which allows different applications to communicate with the model through an API (Application Programming Interface).
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