Summary of Next generation AI for developers with the Microsoft Cloud | KEY03H

00:00:00 - 00:55:00

The Next Generation AI for Developers with the Microsoft Cloud video showcases Microsoft's commitment to using AI to help organizations operate more effectively and to help developers create new innovative apps. The video includes demonstrations of several AI-powered tools and services offered by Microsoft, such as GitHub Copilot, Azure AI, and Microsoft Fabric, which can help developers optimize code, streamline workflows, and deploy scalable solutions. The video highlights the company's focus on privacy, compliance, and security, as well as its commitment to renewable energy. Ultimately, Microsoft aims to provide powerful, reliable, and scalable AI infrastructure that includes purpose-built AI to ensure safety and security as businesses innovate with AI.

• 00:00:00 In this section, Scott Guthrie, Executive Vice President, Cloud + AI at Microsoft, discusses how AI is set to change the way every organization operates and how every existing app is going to be reinvented with AI. He discusses how developers can use Azure, GitHub, and Visual Studio to make it easy to develop AI solutions using GitHub Copilot -- an AI pair programmer that works with all popular programming languages and dramatically accelerates productivity. He then invites Thomas Dohmke, the CEO of GitHub on stage to demonstrate how Copilot improves productivity and provides immersive Copilot chat functionality, Copilot for pull requests in automatic unit test generation scenarios.
• 00:05:00 In this section, a developer showcases the benefits of using GitHub Copilot, Microsoft's AI-powered tool that can generate complex code. The developer demonstrates how Copilot can help debug and provide solutions, allowing users to complete repetitive tasks more quickly. They also introduce the new Copilot Chat tool, which creates unit tests for a user's code and accelerates the development process. Finally, the demo transitions to building AI plugins using Visual Studio and GitHub, which allow developers to customize applications and data within ChatGPT. The example highlights a fictitious e-retail outdoor store that suggests related products to users based on their queries.
• 00:10:00 In this section, a developer demonstrates how they use Microsoft's GitHub Codespaces, along with the new plugin model, to build a plugin for ChatGPT in Python. They explain how the plugin is able to retrieve product information from a JSON file and how the codespace allows for easy debugging of the plugin within VS Code. The demo also showcases how the exact same plugin model can be used for Microsoft 365 Copilot and Bing Chat. This new extensibility model is powerful and enables developers to connect their apps to AI and data.
• 00:15:00 In this section, the speaker discusses how developers can use Azure, along with GitHub Actions, to deploy and scale their AI-powered plugins. They highlight the elasticity of Azure, which offers cloud-native services such as Kubernetes and container-based solutions to help developers scale seamlessly. The speaker uses an example of the OpenAI ChatGPT service, which is deployed using GitHub and Azure combination for its development and Kubernetes on Azure to run and scale. Additionally, the speaker walks through steps for setting up a production version of the plugin using Azure Container Apps, GitHub Actions, and the Azure Developer CLI (AZD) in the terminal. The speaker emphasizes the benefits of the continuous delivery pipeline and GitHub Actions, which allow the team to collaborate, commit changes, test, and deploy updates securely and repetitively.
• 00:20:00 In this section, Scott Guthrie, the Executive Vice President of the Cloud and AI Group at Microsoft, explains how the company was able to develop so many Copilots so fast by building them all on top of one platform: Azure AI. Azure AI includes several categories of AI capabilities spanning from their advanced Azure AI infrastructure to their newest capability, the Azure OpenAI Service. This newest Azure feature enables developers and organizations access to the most advanced AI models in the world. Guthrie emphasizes Microsoft's commitment to privacy, compliance, and security regarding their customers' data being used to ground and fine-tune their AI models. The Azure OpenAI Service is also isolated so that each customer has their instance, so their data and AI models cannot be used by any other organization. He then highlights some customers already using the Azure OpenAI Service and introduces the Azure AI Studio, which they are introducing during this year's Build event, to aid developers in building their own AI solutions.
• 00:25:00 In this section, Seth Juarez showcases how easy it is to create a private company Copilot that understands your data using the Azure AI Studio. Juarez demonstrates the RAG pattern, which Azure has simplified to a few clicks, and how to add data sources including PDF documents and Word files. With this development, users can create a distinct Copilot that understands their applications and organizations' distinct requirements.
• 00:30:00 In this section, Microsoft introduces the Azure AI Prompt Flow, which provides end-to-end AI development tooling for prompt engineering, supporting prompt construction, orchestration, testing, evaluation, and deployment. The tool makes it easy to leverage open source frameworks like Semantic Kernel and LangChain to build AI solutions. The demo showcases how the tool fetches structured and unstructured data from multiple sources and puts it directly into the prompt. The data flows down into the prompt, simulating a Flow Prompt, and the tool includes a prompt flow graph, prompt retrieval node, and model call, which can run directly in the tool, making it straightforward to test responses and deploy models.
• 00:35:00 In this section, the transcript discusses Prompt Flow and Azure AI's end-to-end system for building, testing, and deploying modern AI-driven applications, including a new Azure AI model catalog that allows users to consume open-source AI models. Additionally, a new Azure AI Content Safety service is introduced as an API-based product that can be used to monitor online communities and applications in real time for harmful content, marking a significant step forward in developing responsible AI. The safety system uses a layered approach to safety, including safety built into the model itself, and the Content Safety API can adjust severity levels to suit individual needs.
• 00:40:00 In this section of the video, Sarah Bird showcases the Azure AI Content Safety feature that is already integrated into Azure OpenAI, which is a safety system that rejects harmful queries made to the Contoso bot to protect both users and the company. Bird also talks about the importance of engineering and experimenting with Meta-Prompts, which are system-level prompts that control the model's output. She demonstrates the use of Azure AI Prompt Flow to experiment with Meta-Prompts for the Contoso retail flow, which includes a new safety section, and test two variants for safety and performance before deployment. With Azure AI, Microsoft aims to provide powerful, reliable, and scalable AI infrastructure that includes purpose-built AI to ensure safety and security as businesses innovate with AI.
• 00:45:00 In this section, the speaker explains that Azure has built the largest AI model training system in the world with tens of thousands of interconnected GPUs and fast networking, and was the first cloud provider to deploy and offer NVIDIA Hopper based GPU systems. Azure's InfiniBand cables give it the highest network bandwidth and the lowest network latency of any cloud provider today. Further, Microsoft is committed to being 100 percent reliant on renewable energy by 2025 and carbon negative by 2030. Azure's AI infrastructure is being used for many AI models and experiences besides OpenAI and LLM, including NVIDIA's Omniverse Cloud, which gives enterprises access to full-stack software applications and NVIDIA OVX infrastructure and combine it with Azure's scale and security for building virtual factories.
• 00:50:00 In this section, it is discussed how Microsoft Fabric provides a unified platform for data analytics designed for the era of AI, unifying all analytics tools into a seamless product from data to governance and security, with Microsoft 365 integration. Microsoft Fabric is designed with built-in Copilot support that empowers everyone to quickly find and share insights. As demonstrated in a video, Copilot proposes reports based on natural language descriptions by the user, modifies reports based on the user’s needs, and is fully interactive with Power BI. Microsoft Fabric is another innovative tool by Microsoft that shows how they continue to lead the industry in data analytics and AI.
• 00:55:00 In this section, we learn about Microsoft Fabric, a serverless data management engine that is optimized for advanced AI scenarios. It comes with OneLake, a multi-Cloud data lake built on open formats that is available to every Microsoft tenant. Additionally, Microsoft Fabric is designed to work across Clouds, including AWS and, in the future, Google. The platform is now available in public preview, and Microsoft invites developers and organizations to innovate using AI on Azure in the Microsoft Cloud.