How to create your Copilot with Azure AI Studio in 30 minutes

Who isn't using ChatGPT right now? Who has yet to hear of OpenAI? Admit it :) I'm sure many have tried using these technologies at least once. But we often lack fresh knowledge in these models, or specific knowledge about our topic, for which we want to communicate with the model, for example, our knowledge base. In this article, we will look at how you can create your model based on ChatGPT technology using the powerful tools of Azure AI Studio.

Azure AI Studio is a powerful integrated environment for developing and deploying artificial intelligence (AI) models and programs on the Microsoft Azure platform.

It provides a wide range of tools and services that simplify the process of creating, training, and deploying AI models, including features such as:

1. Integrated Development Environment (IDE): Azure AI Studio provides a convenient intuitive interface for developing AI models. This includes the ability to write code, visualize data, configure model parameters, and much more.
2. Cloud resources: Azure AI Studio makes it easy to scale computing resources to train and deploy models. With cloud computing, you can quickly and efficiently process large amounts of data and run calculations in real-time.
3. Visualization and monitoring: the studio provides tools to visualize the results of model training and monitor their performance in real-time. This helps developers better understand the behavior of models and optimize their performance.
4. Integration with other Azure services: Azure AI Studio is tightly integrated with other Azure platform services and tools, such as Azure Machine Learning, Azure Cognitive Services, and Azure Data Lake. This allows you to create complex AI solutions using various services and tools on the Azure platform.

With Azure AI Studio, developers can easily build, train, and deploy various AI models and applications, from classic machine learning algorithms to deep neural networks and natural language processing applications.

In this article, we will take the following steps:

1. Create a project in Azure AI Studio: We'll start by creating a new project in Azure AI Studio and configuring it to develop our model.
2. Model creation and selection: We will select a model in Azure AI Studio with which we will work.
3. Adding test PDFs: We will upload several test PDFs that will be used to test the functionality of our model. These documents can contain different types of textual information to test how well the model can handle various of data.
4. Testing with new data: We will test our model using the downloaded PDF documents. We will test how the model handles queries based on this data and provide appropriate responses.
5. App Deployment to Azure Web App: After successful testing, we'll deploy our app to Azure Web App to make it available to use from a web browser. We will demonstrate the process of deploying and configuring an application in the Azure cloud.

After completing all these steps, we will receive a fully functional application capable of processing requests based on PDF documents using a model and a web interface.

How to start working with AI studio, everything is simple register or log in with a Microsoft account to the site? - https://ai.azure.com/

After entering Azure AI studio, we can create a new project, of course, by clicking on the New project button, the interface is generally very convenient and intuitive.

To create a new project, we will need a new hub.

Hub is a collaboration environment for your team to share your project work, model endpoints, compute, connections, and security settings

Select a subscription, resource group, and project name. At this stage, we do not need Azure search yet

Click to create a project. And we go to the project interface.

For further testing, we need a model that will be used by us, we go to the catalog of models, and... your eyes may run out, there are a lot of them here

Let's start with gpt-4o

Go to the model and click deploy:

After the model is deployed, we can already interact with it by going to the chat, and for example, find out how it is doing:

What is the next step? Let's add our information to be processed by the model, for this, we need to add a data source:

After that, it is necessary to add how exactly we will receive our data. There are several options, these are ordinary files that can be downloaded and blobs, etc. I chose the easiest option - I uploaded my resume:

Now we need to create an Azure AI Search service, you can use the current one or create a new one, you can also choose the name of the index and the machine on which it will be run - run indexing jobs:

A sample diagram of the solution we are building:

We press further and we can watch how our data is indexed and enters the AI search

After Azure AI Studio adds our data, we can try to query the chat for the information we've already added.

Now we can safely deploy our model in the application, and this is done with one button - we click “deploy to a web app”:

We need to choose a subscription, group resource, and hosting plan for our program.

And you can go and enjoy the minimalistic interface:

Conclusions: In this article, we considered the process of creating and deploying a custom model based on OpenAI technology using Azure AI Studio. We started working with the model immediately after its deployment and then added new test documents to test the model's performance with a variety of data. After successful testing, we deployed our application to Azure Web App, which allowed us to make it available for use from a web browser.

Examples of use:

1. Creating a knowledge base: We can use our Gpt4o-based model to create a knowledge base based on our material library. Users can ask questions based on text documents and the model will provide appropriate answers based on our data.
2. Customer Support: Our model can be used to automate responses to customer inquiries in various industries, such as technical support, customer service, and product or service consulting.
3. Automatic analysis of documentation: We can use a model for automatic analysis and processing of text documentation, which will significantly reduce the time to search for the necessary information and improve the process of working with documents.

In general, creating and deploying your model in Azure AI Studio opens up wide opportunities for creating intelligent applications and automating processes based on processing text data.

We also have a channel on Azure in Telegram, join the community? - https://t.me/azureuacommunity