Azure OpenAI Studio: A Comprehensive Guide to Building Smarter Copilots

Azure OpenAI Studio is a powerful tool that enables developers to build smarter copilots for various applications. It's built on top of the OpenAI API, which provides access to the latest AI models and technologies.

With Azure OpenAI Studio, you can create custom copilots that can perform tasks such as text summarization, language translation, and even content generation. This is made possible by the studio's ability to integrate with various Azure services.

One of the key benefits of Azure OpenAI Studio is its ease of use. Even developers with limited AI experience can quickly get started and build their own copilots using the studio's intuitive interface.

To get started with Azure OpenAI Studio, you'll first need to create your model in Azure OpenAI. This involves selecting the GPT-4 or GPT-4-32k models and completing required fields like model version or tokens per minute rate limit.

You can create your model using Azure AI Studio, a feature currently in preview. This is where you'll start building your deployments.

Once your model is created, you can deploy it as a new copilot in Copilot Studio, also in preview. You'll need to agree to connect your Azure OpenAI subscription with your Copilot tenant, which may lead to data being processed outside your Copilot Studio tenant's geographic region.

To start working on the copilot, you'll need to create a new copilot and enable the option to boost conversations with generative answers.

To deploy your model as a new copilot in Copilot Studio, you'll need to agree to connect your Azure OpenAI subscription with your Copilot tenant, which may lead to data processing outside your Copilot Studio tenant's geographic region.

A pop-up screen will guide you through this process, and you'll be able to start working on your copilot once you've agreed to the terms.

You'll need to create a new copilot and enable the option to boost conversations with generative answers to get started.

To use Copilot, you'll need to create a new copilot in Copilot Studio, a feature currently in preview.

You'll see a pop-up screen asking you to agree to connect your Azure OpenAI subscription with your Copilot tenant, which may lead to data being processed outside your Copilot Studio tenant's geographic region.

Create a new copilot and enable the option to boost conversations with generative answers.

You can test your copilot even before publishing it, and you'll get the same result as when you tested the model in Azure OpenAI.

Thanks to the Copilot designer, you can see which topic has been triggered and the nodes that have been executed.

Make sure to use the GPT-4-32k model when creating the model in Azure OpenAI, as this is required for the Copilot Studio integration to work.

To fine-tune a model, select one of the supported models, such as babbage-002, davinci-002, or gpt-35-turbo, in Azure OpenAI Studio's Create custom model wizard.

You'll need to provide a JSONL file as the training dataset, which can be a local file or a public online resource.

To configure the connection, you'll need to select the connection to the Azure OpenAI model. This is where things get interesting.

You'll need to specify the deployment, API version, and maximum tokens in response. For example, in the General tab, you would select "northwind-model" as the deployment and "2023-06-01-preview" as the API version.

Maximum tokens in response is also crucial, as it determines the number of tokens used in a response. In our example, we used 800 tokens.

Temperature and Top P parameters are also important, but be careful when adding them. They are numbers, so add them as numbers, not strings.

To configure Copilot, you'll need a few essential things.

You'll require an Azure subscription with access to Azure Open AI services.

To fine-tune a model, you'll also need an Azure Open AI resource created in one of the supported regions for fine-tuning.

This resource should have a supported deployed model.

Additionally, you'll need the Cognitive Services OpenAI Contributor role.

It's worth considering whether you really need to fine-tune a model, as it can be a complex and resource-intensive process.

Customizing Copilot is a crucial step in harnessing the full potential of Azure OpenAI Studio.

To create a new copilot, navigate to Copilot Studio, which is currently in preview.

A pop-up screen will appear, asking you to connect your Azure OpenAI subscription with your Copilot tenant, which may lead to data processing outside your Copilot Studio tenant's geographic region.

This is a necessary step to enable the creation of a new copilot.

Once connected, you can start working on your copilot by creating a new one and enabling the option to boost conversations with generative answers.

To deploy Copilot, first open Azure OpenAI Studio by clicking on "Go to Azure OpenAI Studio" (or navigate directly to oai.azure.com).

You can choose to deploy a base model or a fine-tuned model, depending on your needs.

To deploy a base model, select from the available OpenAI models in your region. A page will pop-up with all OpenAI models available in the region, where you can select the one you want to deploy and click "confirm".

Configure your deployment by filling in the required fields: Deployment Name, Model Version, Deployment Type, Tokens per Minute Rate Limit, and Content Filter.

Here are the options for Deployment Type:

We can test our copilot even before publishing it, after saving the changes.

Once we've made the necessary changes, we can test our copilot to see how it's working. The Copilot designer allows us to see which topic has been triggered and the nodes that have been executed.

Remember to use the GPT-4-32k model when creating the model in Azure OpenAI, as this is required for the Copilot Studio integration to work.

To deploy your Copilot, you'll need to create a deployment in Azure OpenAI Studio. This involves selecting a model, configuring the deployment, and finalizing the deployment.

You can choose from various deployment types, including Global Standard, Global Batch, Standard, and Provisioned Managed. Each type has its own configuration options, such as tokens per minute rate limit and content filter.

Here's a summary of the deployment options:

Once you've selected your deployment type, you can configure the deployment settings, such as model version, tokens per minute rate limit, and content filter. After filling in the fields, click "deploy" to finalize the deployment.

After completing the configuration, click "Deploy." The model will appear on the "Deployments" page. To test the model, click "Open in Playground", which will redirect you to the "Chat session" for testing.

Azure OpenAI Studio is a powerful tool that provides API access to OpenAI's language models, including GPT-4 and GPT-3.5 Turbo.

You can access the service through REST APIs, the Python SDK, or Azure OpenAI Studio itself, making it a versatile option for businesses and developers.

Azure OpenAI Studio is built on the enterprise-level security of the Azure platform, ensuring that your data is protected and secure.

Azure OpenAI is a powerful tool that provides API access to OpenAI's language models, including GPT-4 and GPT-3.5 Turbo.

You can access Azure OpenAI through REST APIs, the Python SDK, or Azure OpenAI Studio, which offers a user-friendly interface for getting started.

The Azure platform provides enterprise-level security, making it a reliable choice for businesses.

To get started with Azure OpenAI, you'll need to create an Azure Open AI resource using the wizard, being mindful of the region you choose.

Currently, only North Central US and Sweden Central support the fine-tuning capability, so choose one of these regions for optimal results.

By combining the power of OpenAI's language models with Azure's security, scalability, and enterprise features, you can create and deploy models that meet your unique needs.

Azure OpenAI offers built-in security to protect your applications and data.

You can customize network configurations to ensure your applications are tailored to your specific requirements.

With flexible deployment options, you can deploy your models in the way that best suits your needs.

This combination of cutting-edge language models and robust infrastructure empowers developers to build AI-driven solutions that are tailored to their needs.

By following these steps, you can effectively create, manage, and deploy your models, ensuring your applications benefit from the capabilities of both OpenAI's technology and Azure's infrastructure.