To leverage the scale of large language models (LLMs) and power your solution’s AI capabilities, it is essential to create an Azure OpenAI service. This cloud platform offers a range of services that allow you to deploy and access advanced AI models, enabling various applications such as translation, summarization, conversation, and more.

Creating an Azure OpenAI service allows you to harness the potential of large language models to enhance your solution’s AI capabilities. By connecting your own data, calling functions, and leveraging the services provided by Azure OpenAI, you can improve workflow and enhance the overall functionality of your solution.

One of the key benefits of Azure OpenAI is the ability to seamlessly integrate its advanced language, image, and speech models into your solution. These models provide advanced natural language processing (NLP), computer vision, and speech-to-text capabilities, allowing you to build intelligent and innovative solutions.

What is Azure OpenAI Service?

Azure OpenAI Service is Microsoft’s cloud solution for deploying, customizing, and hosting generative AI models. It brings together the best of OpenAI’s cutting edge models and APIs with the security and scalability of the Azure cloud platform.

Azure OpenAI Service provides REST API access to OpenAI’s powerful language models including the GPT-4, GPT-4 Turbo with Vision, GPT-3.5-Turbo, and Embeddings model series.

These models can be easily adapted to your specific task including but not limited to content generation, summarization, image understanding, semantic search, and natural language to code translation.

Users can access the service through REST APIs, Python SDK, or our web-based interface in the Azure OpenAI Studio.

Azure OpenAI supports many models that can serve different needs. These models include:

• GPT-4 models are the latest generation of generative pretrained (GPT) models that can generate natural language and code completions based on natural language prompts.
• GPT 3.5 models can generate natural language and code completions based on natural language prompts. In particular, GPT-35-turbo models are optimized for chat-based interactions and work well in most generative AI scenarios.
• Embeddings models convert text into numeric vectors, and are useful in language analytics scenarios such as comparing text sources for similarities.
• DALL-E models are used to generate images based on natural language prompts. Currently, DALL-E models are in preview. DALL-E models aren’t listed in the Azure OpenAI Studio interface and don’t need to be explicitly deployed.

Models differ by speed, cost, and how well they complete specific tasks.

In many cases, models can be used as-is.

For example, in Azure OpenAI Service, you can deploy a GPT-4 model and immediately start using it from an application.

However, you can also use an existing model as a foundational model – a starting point for further training with your own data. This approach is called fine-tuning, and it enables you to train a custom model that builds on the pre-trained model, but which is tuned to data that is relevant for your particular scenario.

For example, a legal firm might fine-tune a model with the text from existing contracts and other proprietary legal documents to train a model that is optimized for generating contractual content.

Features overview

What is OpenAI Services?

OpenAI Services refer to a suite of powerful artificial intelligence (AI) tools and capabilities provided by OpenAI, an organization dedicated to advancing AI research and development. These services enable developers and businesses to leverage cutting-edge AI models for various tasks. Here are some key aspects of OpenAI Services:

1. Natural Language Processing (NLP):
   • OpenAI offers state-of-the-art language models that excel in understanding and generating human-like text. These models can be used for tasks such as chatbots, sentiment analysis, language translation, and content generation.
   • Notable examples include GPT-4 (Generative Pre-trained Transformer 4) and GPT-3, which have demonstrated remarkable language understanding and creativity.
2. Computer Vision:
   • OpenAI provides models for analyzing and interpreting visual data. These models can recognize objects, detect faces, and perform image classification.
   • DALL-E is an intriguing model that generates unique images from textual descriptions.
3. Customization and Fine-Tuning:
   • Developers can fine-tune OpenAI models to suit specific use cases. This allows customization and adaptation to domain-specific tasks.
   • Fine-tuning involves training the model on specific data or adjusting its parameters to achieve better performance.
4. Integration with Azure:
   • Azure OpenAI Service allows seamless integration of OpenAI models into applications hosted on Microsoft Azure.
   • Developers can access these models via APIs, making it easier to incorporate AI capabilities into their software.
5. Applications:
   • OpenAI Services find applications in various domains, including:
     • Conversational AI: Building chatbots, virtual assistants, and interactive interfaces.
     • Content Generation: Creating articles, summaries, and creative writing.
     • Data Grounding: Associating textual information with real-world entities.
     • Research and Exploration: Experimenting with novel AI approaches.

In summary, OpenAI Services empower developers to harness the potential of advanced AI models, enhancing their applications and driving innovation in the field of artificial intelligence. 

Difference between Open AI services and Azure OpenAI services?

Demo: Creating Azure Open AI Service:

Below are the steps required:

1. Request access for Azure Open AI service.
2. Create Azure OpenAI services from Azure Portal.
3. Use Azure OpenAI services to create a Model.
4. Use Azure AI Studio to manage Azure OpenAI services

Requesting access for Azure Open AI service:

Request for access from the link:

Request Access to Azure OpenAI Service (microsoft.com)

Create Azure OpenAI services from Azure Portal

To get started with Azure OpenAI, you will need to create a new deployment and for this you need to do the followings:

• Request access for Azure OpenAI services (requested in step1)
• Create a Azure OpenAI resource in Azure Portal (Below is the process)

Use Azure OpenAI Studio to create a model.

Once you have created a Resource for Azure OpenAI service, now you can get started with Azure OpenAI service.

To get started you will need to create a new deployment and this can be done with Azure OpenAI Studio.

Why do you need a Deployment?

Deployments provide endpoints to the Azure OpenAI base models, or your fine-tuned models, configured with settings to meet your needs, including the content moderation model, version handling, and deployment size.

What is Azure OpenAI Studio?

Developers can work with these models in Azure OpenAI Studio, a web-based environment where AI professionals can deploy, test, and manage LLMs that support generative AI app development on Azure.

Click on below link to open Azure OpenAI services in Azure OpenAI Studio.

You will see below screen.

Click on Create new deployment.

What is Chat Playground?

Within Azure OpenAI Studio, you can deploy large language models, provide few-shot examples, and test them in Azure OpenAI Studio’s Chat playground.

Use Azure AI Studio(preview)

Go to the Azure OpenAI Studio and click on the highlighted link below.

Below is the screen.

We already have created Azure OpenAI service (Step2)

We will click on the Advance option. To get started with Azure AI Studio, check here Getting started with Azure AI Studio

Once you complete these steps, you can see the screen below.

It has more features as compared with Azure OpenAI studio.

This Chat Solution (Referred as Copilot) uses the Deployment OpenAI-demo (which we created in our demo, created Azure Open AI service)

In this example, an Azure OpenAI Service model is used to power a copilot application that can be used to generate original content in response to user prompts.

Wrapping up!

Conclusion:

Creating an Azure OpenAI service is crucial if you want to leverage the potential of large language models in your solution. This service provides advanced AI models, scalability, and seamless integration into your workflow, enabling you to build intelligent and innovative solutions.

Our discussion of Azure Open AI services covered how to get started, but there is much more to explore.

As we dive into this exciting AI landscape, stay tuned for more insightful and actionable content that will help you stay ahead of the curve.

Check out below post learn more about Gen AI:

Getting started with Azure AI Studio

Create your own Copilot that uses your own data with an Azure OpenAI Service Model

Build your first Prompt Flow

Build and deploy a Q&A Copilot with Prompt Flow