Microsoft’s Cutting-edge AI Applications Set to Transform Microsoft Azure
Generative AI: AI that creates
Generative AI is a powerful technology that has the potential to revolutionize various industries, such as healthcare, manufacturing, retail, and education. It goes beyond what we can currently imagine. It transforms entire industries and changes the way we live and work. At the heart of this innovation are developers who are pushing the boundaries of what is possible and creating new value for businesses and society. Microsoft’s Azure platform provides a secure and responsible environment for developers to build with generative AI.
Latest Innovations: AI and Apps
Microsoft is diving deep into the latest technologies in application development and AI to enable the next wave of innovation. The focus is on providing state-of-the-art AI capabilities and empowering developers with tools and resources to build securely and responsibly. Azure offers the best cloud-native app platform to harness the power of AI in critical business applications. AI-assisted developer tooling helps developers ship their code securely. Microsoft has made significant announcements in all key areas to empower organizations to lead in the era of AI.
Generative AI with Azure
Azure OpenAI Service, integrated with Microsoft Azure, allows developers to apply generative AI to their data and integrate AI-powered experiences into their applications. OpenAI’s GPT-4, a cutting-edge AI model, is now available in Azure OpenAI Service, offering developers new capabilities to leverage generative AI. Azure AI Studio provides a user-friendly interface for developers to ground powerful conversational AI models on their data. Azure Cognitive Search enables users to discover information using natural language-based app interfaces. The service also supports vectors and plugins to simplify integration with external data sources.
Advancements in Azure Machine Learning
Azure Machine Learning offers new capabilities for data scientists to leverage generative AI in their work. Foundation models in Azure Machine Learning allow data scientists to fine-tune, evaluate, and deploy pre-existing models from various sources. Azure Machine Learning prompt flow provides a streamlined experience for creating prompt workflows using different language models and data sources. These advancements provide data scientists with a comprehensive repository of models and tools within Azure.
Running AI-Powered Apps on Azure
Azure provides a cloud-native platform to run and scale applications while integrating Azure’s native AI services. Azure Kubernetes Service (AKS) offers control and flexibility for developing and deploying intelligent, cloud-native apps. Long-term support for Kubernetes enables customers to stay on the same release for an extended period. Azure Linux as a container host operating system optimizes AKS. Azure Container Apps, a serverless platform, allows microservices and containerized applications to run on Azure. These services make building intelligent cloud-native apps seamless and natural.
Integrated AI-Based Tools for Developers
Microsoft invests in tools and resources to simplify processes and increase developer velocity and scale. GitHub Copilot, an AI developer tool, helps developers code faster by providing suggestions and snippets. Microsoft Dev Box and Azure Deployment Environments simplify development setup and streamline performance. GitHub Advanced Security for Azure DevOps integrates automated security checks into workflows, ensuring code quality and preventing vulnerabilities. These tools empower developers to focus more on strategic work and less on mundane tasks.
Secure and Trusted AI Experiences
Security is crucial when building, deploying, and running intelligent applications. Microsoft invests in features and capabilities to enable earlier implementation of security in the software development lifecycle. GitHub Advanced Security for Azure DevOps provides automated security checks for code repositories, scanning for vulnerabilities and preventing the inclusion of sensitive information. Responsible AI is also a priority, and Microsoft offers products and features to improve accuracy, safety, fairness, and explainability across the AI development lifecycle. Azure AI Content Safety helps build safer online environments, and the responsible AI dashboard in Azure Machine Learning supports model monitoring and data integrity tests.
Responsible AI: Ethical development
Building AI systems responsibly is of utmost importance. Microsoft has been committed to ensuring responsible AI design for almost seven years. It involves considering ethical implications throughout the development process. Policies alone are not enough, and Microsoft provides tools and engineering systems to facilitate responsible AI development. This includes features to enhance accuracy, safety, fairness, and explainability.
Azure AI Content Safety: Safer online spaces
Azure AI Content Safety, currently in preview, helps developers create safer online environments. It can detect and assign severity scores to unsafe images and text across languages. This feature assists businesses in prioritizing content moderation. Azure AI Content Safety is integrated across various Microsoft products, including Azure OpenAI Service and Azure Machine Learning. It enables users to evaluate and moderate content in prompts and generated content, ensuring responsible AI usage.
Responsible AI Dashboard: Ensuring fairness
The responsible AI dashboard in Azure Machine Learning supports text and image data in preview. It provides users with the means to identify model errors, understand performance and fairness issues, and explain machine learning model types, such as text and image classification and object detection. This dashboard allows users to monitor models and production data for model and data drift, perform data integrity tests, and intervene when necessary. It ensures AI systems are fair and accountable.
Enabling Secure Deployments: 3 Key Measures
GitHub Advanced Security for Azure DevOps: Integrated security
GitHub Advanced Security, soon available in preview for Azure DevOps, brings automated security checks into the development workflow. It combines code scanning powered by CodeQL, secret scanning, and dependency scanning. Code scanning detects vulnerabilities, secret scanning prevents sensitive information from being included in code repositories, and dependency scanning identifies vulnerabilities in open-source dependencies. These features enable developers to build secure applications by addressing potential security issues early in the development process.
Privacy and Data Protection: AI with care
Privacy and data protection are vital considerations when working with AI. Microsoft’s commitment to privacy and adherence to regulations like the General Data Protection Regulation (GDPR) shape their approach. They emphasize the need for tools and engineering systems that facilitate responsible AI development. Microsoft ensures that AI systems respect privacy and adhere to regulations while delivering powerful AI capabilities.
Azure Deployment Environments: Trustworthy containers
Azure Deployment Environments provide a secure and trustworthy environment for deploying applications. Microsoft supports the use of HashiCorp Terraform and Azure Resource Manager to facilitate the deployment of trusted containers to Azure. By pairing developer-first tooling with secure deployment practices, Azure enables developers to build and deploy applications with confidence.
These measures aim to integrate security, privacy, and responsible AI practices into the development process. Microsoft prioritizes building secure, trustworthy, and ethical AI systems while empowering developers with the tools they need to thrive in this rapidly evolving field.
