Azure OpenAI What's New: Features and Use Cases Explained

Azure OpenAI has been making waves with its innovative features and use cases. One of the most significant updates is the integration of OpenAI's models with Azure Cognitive Services.

This integration enables developers to leverage OpenAI's advanced language models, like the GPT-3.5 model, to build more sophisticated AI applications. The GPT-3.5 model is particularly useful for tasks that require high levels of language understanding and generation.

By tapping into Azure's scalable infrastructure, developers can now create more powerful and efficient AI models. This is a game-changer for industries that rely heavily on language processing, such as customer service and content creation.

The Azure OpenAI platform also offers a range of pre-built models and templates to help developers get started quickly. These templates can be used to build applications for tasks like chatbots, language translation, and text summarization.

Azure AI brings safety by default to GPT-4o mini, featuring prompt shields and protected material detection, ensuring a safer experience for developers.

The Azure AI Content Safety features have been improved with an asynchronous filter, allowing for faster and more efficient processing without compromising safety.

GPT-4o mini is now available using global pay-as-you-go deployment at 15 cents per million input tokens and 60 cents per million output tokens, making it a cost-effective option for variable workloads.

Azure OpenAI Service offers GPT-4o mini with 99.99% availability, the same industry-leading speed as OpenAI, and 15M tokens per minute (TPM) throughput.

Fine-tuning for GPT-4o mini is now available, allowing customers to further customize the model for their specific use case and scenario, delivering exceptional value and quality at unprecedented speeds.

Azure AI now offers data residency for all 27 regions, providing customers with flexibility and control over where their data is stored and processed.

Here are the key updates:

Azure AI Content Safety features are now 'on by default' for GPT-4o mini on Azure OpenAI Service. This means you can use these features to safeguard your generative AI applications without having to manually enable them.

We've invested in improving the throughput and speed of Azure AI Content Safety capabilities, including the introduction of an asynchronous filter. This allows you to maximize the advancements in model speed while maintaining safety.

Azure AI Content Safety is already supporting developers across industries, including game development, tax filing, and education. This shows the versatility and effectiveness of these safety features in real-world applications.

Our Customer Copyright Commitment will apply to GPT-4o mini, giving customers peace of mind that Microsoft will defend them against third-party intellectual property claims for output content. This commitment provides an added layer of protection for developers using GPT-4o mini.

The Tech Community is abuzz with excitement about Azure OpenAI's latest updates. Azure OpenAI Service now offers GPT-4o mini with a 128K context window and improved multilingual capabilities.

GPT-4o mini is significantly smarter than GPT-3.5 Turbo, scoring 82% on Measuring Massive Multitask Language Understanding (MMLU) compared to 70%. This means it can deliver more accurate and informative responses.

Azure OpenAI Studio Playground allows you to try GPT-4o mini at no cost, giving you a chance to experience its capabilities firsthand. This is a great way to test the waters and see how it can enhance your applications.

GPT-4o mini is available simultaneously on Azure AI, supporting text processing capabilities with excellent speed. Image, audio, and video capabilities are coming later, but for now, you can focus on text-based applications.

Microsoft Teams meeting management can be improved with Azure OpenAI Service, through features like meeting scheduling, transcriptions, and speech helper. This can make meetings more productive and efficient.

Safety features are now by default for GPT-4o mini, ensuring that the model is used responsibly and ethically. This is a key aspect of Azure OpenAI's commitment to trust and transparency.

The Azure OpenAI Service is part of Azure Cognitive Services, which offers a range of AI-powered capabilities like translation, natural language processing, and image recognition. This can be used to automate tasks and enhance applications.

Consuming Azure OpenAI is a straightforward process that can be done in just a few clicks. You can access the Azure OpenAI web app and generate code in JSON to help you understand the service better.

To consume Azure OpenAI, you can apply simple or semantic search on your private data or public data from the internet. The service uses models built on text, code, or embeddings.

To create an Azure OpenAI model using your own data, you'll need to create an Azure AI Search and an Azure Blob Storage with a container. You can then deploy your trained model to the Azure OpenAI instance and access the Chat Playground feature.

Here are the steps to create an Azure OpenAI model using your own data:

1. Create an Azure AI Search.

2. Create an Azure Blob Storage with a container.

3. Deploy your trained model to the Azure OpenAI instance.

4. Access the Chat Playground feature and choose Azure Blob Storage as your data source.

5. Configure properties like Blob storage, Container, and Azure OpenAI.

6. Provide a suitable index name and select options other than Vector search.

You can also consume Azure OpenAI as a REST API using Azure Data Factory. To do this, you'll need to add a web activity to your ADF pipeline and configure it to make an HTTP request to the Azure OpenAI REST API endpoints.

Azure OpenAI provides a wide range of features, including content generation, summarization, code generation, and semantic search. These features can be used in various industries, such as healthcare, finance, retail, and manufacturing.

Here are some examples of how Azure OpenAI can be utilized:

By following these steps and utilizing the features of Azure OpenAI, you can unlock the full potential of this powerful service.

Azure OpenAI offers global pay-as-you-go deployment at 15 cents per million input tokens and 60 cents per million output tokens, making it a cost-efficient choice for variable workloads.

With global pay-as-you-go deployments, customers can upgrade from existing models to the latest models without having to change their region. This flexibility is a game-changer for businesses with fluctuating workloads.

Azure OpenAI provides the highest possible scale, offering 15M tokens per minute (TPM) throughput for GPT-4o mini and 30M TPM throughput for GPT-4o. This means that businesses can process large amounts of data quickly and efficiently.

Here are some key features of Azure OpenAI:

Azure OpenAI also allows businesses to use their own data with the service, making it a powerful tool for data analysis and processing.

Azure OpenAI Service offers a wide range of use cases that can be applied to various industries and applications.

One of the key use cases is analyzing user inputs, which can be done through classification, sentiment analysis, and entity extraction. This can be used to analyze product feedback, support calls, and transcripts, and even improve text-based searches.

Azure OpenAI Service allows users to develop conversational agents that can interact with users within a specific range of topics. The agent will provide responses based on reliable sources such as internal company documentation or technical support documents.

Users can also develop chatbots that use answers from reliable sources like internal company records or technical support documents. The chatbot can only provide responses to specific questions within a defined scope.

Code generation or transformation scenarios are another area where Azure OpenAI Service can be applied. This includes changing one type of programming code into another, making a list of instructions for functions, and even turning words into SQL language.

Azure OpenAI Service can also be used for summarization, where users can submit content for summarization, but only for specific topics stored within the application. The available topics include summarizing internal company documents, call center transcripts, technical reports, and product reviews.

Here are some examples of use cases for Azure OpenAI Service:

Azure OpenAI Service can also be used to generate journalistic content or assist in rephrasing journalistic content within particular topics. However, it cannot be used for creating content on any general topic or promoting political campaigns.

Azure OpenAI's Multimodal Feature is a game-changer for AI-powered applications. It allows the model to understand and process information from various sources, including text, images, audio, and other data formats.

The Azure OpenAI model, specifically the gpt-4o model, offers multimodal support, making it possible to incorporate multiple types of data into a single interaction.

You can pass multiple images as well, which is a huge advantage for applications that require visual input.

The Azure OpenAI can incorporate a list of base64-encoded images or image URLs with the message, making it easy to integrate with existing systems.

Spring AI's Message interface facilitates multimodal AI models by introducing the Media type, which encompasses data and details regarding media attachments in messages.

Here are some key benefits of Azure OpenAI's Multimodal feature:

The Azure OpenAI model can generate responses like this: "You can pass multiple images as well." when given an input like the multimodal.test.png image along with the text message "Explain what do you see on this picture?"

Chat Properties give you control over the chat experience.

You can adjust the chat's personality, tone, and language style to fit your needs.

Azure OpenAI's chat properties allow you to set the chat's tone to be more formal or casual, depending on your requirements.

This feature is particularly useful for applications that require a specific tone, such as customer service or educational content.

You can also customize the chat's language style to be more conversational or direct.

The Azure OpenAI Service is a powerful artificial intelligence service that allows users to create and deploy AI models within the Microsoft Azure platform.

It integrates OpenAI's language models and services into Microsoft Azure applications and platform, enabling developers to build advanced applications that automate tasks and process large amounts of data quickly.

With this integration, developers can utilize the power of OpenAI's language models to build applications that interact with customers in natural ways, opening new possibilities for businesses looking to leverage AI solutions for their operations.

This groundbreaking technology significantly reduces the cost associated with developing and deploying complex AI projects, making it more affordable for organizations of all sizes to get started on their own AI projects.

Developers can create and deploy AI models within the Azure platform with minimal effort, allowing applications to be created faster than ever before.