Azure OpenAI Service: Don’t Miss the AI Revolution

This article will cover the Azure OpenAI Service and its potential applications. First, I will introduce Azure and highlight the types of generative AI models available within this service. Then, I will explain how these models integrate into Azure’s ecosystem. Finally, I will present various business use cases identified by Microsoft and Devoteam, especially in the data field.

Understanding Azure OpenAI Service and its origins

Have you heard about OpenAI and its advancements like ChatGPT, GPT-3, and GPT-4? OpenAI was established in California in 2015. Originally, it was a non-profit organisation but became for-profit in 2019. One of its founders is Elon Musk. OpenAI aims to develop and promote AI that benefits humanity. In 2019, Microsoft partnered with OpenAI. This collaboration accelerated OpenAI’s growth using the Azure platform.

Different generative AI models within Azure OpenAI Service

The Azure OpenAI Service features three main generative AI models. The first type includes generative text AI models, such as GPT-3 and GPT-4. GPT-4, notably, is multimodal and can accept both text and images as inputs. The second type is Codex, which stems from GPT models and powers GitHub Copilot. Codex assists with coding through natural language commands. The third type is DALL-E, a model that generates images based on text input.

How these AI models fit into Microsoft’s ecosystem

Azure provides services for developers and business users alike. Users can create machine learning models with Azure Machine Learning or customise pre-trained models from the Cognitive Services suite. Notably, OpenAI is part of this suite. Additionally, these models integrate seamlessly with the Power Platform. For example, Power Virtual Agent helps build chatbots using pre-trained AI models. Furthermore, Microsoft 365 and Dynamics applications incorporate these AI capabilities. Teams Premium, for instance, can now automatically generate meeting notes, which is highly practical.n this article, we will talk about the Azure service at OpenAI. We will start by presenting in broad strokes what Azure is and what types of generative AI models are present within the service, as well as how this service fits within Azure. Then, we will present various business use cases that have been reported by Microsoft, as well as use cases that we have identified at Devoteam, particularly in the field of data.

Business use cases for Azure OpenAI Service

Before diving into specific business use cases, it is important to understand these technologies. They act as co-pilots, helping users work more efficiently and complete repetitive tasks faster. A recent study highlighted GitHub Copilot’s impact on productivity. Developers using this tool completed tasks 55% faster than those who did not. This technology enhances productivity and allows users to focus on core work. However, a gap may form between users who adopt these tools and those who do not.

Exploring content generation use cases

Content generation is the first major category of use cases. Organisations can transform existing FAQs into chatbots for better customer interaction. Unlike traditional chatbots, these AI models can adapt to user-specific requests. This helps improve response quality, especially in call centres or support services. Additionally, AI tools can enable live automatic translations, beneficial for companies engaged in global activities. Reports already show how call centres use AI to eliminate accents, making interactions smoother. Support operators can also access AI-driven tools for faster searches and better responses. Sentiment analysis can be performed on recorded transcriptions. These transcriptions can also be summarised, showcasing the wide range of possibilities.

Summarisation capabilities with Azure OpenAI Service

The next major category is summarisation. This feature proves especially valuable in industries like finance, where summarising reports is common. ChatGPT can generate quick summaries, saving valuable time. Teams Premium uses AI to summarise meetings effectively. Moreover, AI tools can personalise training content for employees on various topics. These can be delivered in fast-track modes, making training more efficient. The potential for applications continues to grow.

How AI supports code generation

Code generation is another major use case. GitHub Copilot, powered by Codex, improves developer productivity. Additionally, non-developers can now interact with databases using natural language. Their requests are then converted into SQL queries automatically. AI tools can also generate code documentation and complete entire sets of documentation. For example, GPT-4 can detect vulnerabilities in code. The director of Coinbase used it to scan Ethereum’s code, revealing an old patched vulnerability. This raises questions about AI’s potential and its capacity to highlight past information.

Semantic search possibilities

Semantic search is another noteworthy use case. Organisations often possess large volumes of documents and emails. AI tools help search through these files to find relevant answers quickly. Users can think of these tools as digital librarians. Semantic search supports contract analysis and other complex document reviews. Its potential applications span various areas.

Other noteworthy use cases

Finally, there are specific use cases related to data science and BI. One significant application is multimodal learning. Users can input images into GPT-4 and receive comments or analysis on graphs and reports. Additionally, providing a basic database schema allows GPT-4 to generate reports or offer ideas for BI visuals. Code migration is another helpful application. For example, the tool can convert SQL queries into Python code effectively. DALL-E models also offer training data generation, essential for creating robust deep learning datasets. Brainstorming sessions can become more productive by using GPT-4 to develop new data-related ideas. These examples only begin to show the extensive possibilities.