The Future of AI: Reduce AI Provisioning Effort - Jumpstart your solutions with AI App Templates
In the previous post, we introduced Contoso Chat – an open-source RAG-based retail chat sample for Azure AI Foundry, that serves as both an AI App template (for builders) and the basis for a hands-on workshop (for learners). And we briefly talked about five stages in the developer workflow (provision, setup, ideate, evaluate, deploy) that take them from the initial prompt to a deployed product. But how can that sample help you build your app? The answer lies in developer tools and AI App templates that jumpstart productivity by giving you a fast start and a solid foundation to build on. In this post, we answer that question with a closer look at Azure AI App templates - what they are, and how we can jumpstart our productivity with a reuse-and-extend approach that builds on open-source samples for core application architectures.
AI reports: Improve AI governance and GenAIOps with consistent documentation
AI reports are designed to help organizations improve cross-functional observability, collaboration, and governance when developing, deploying, and operating generative AI applications and fine-tuned or custom models. These reports support AI governance best practices by helping developers document the purpose of their AI model or application, its features, potential risks or harms, and applied mitigations, so that cross-functional teams can track and assess production-readiness throughout the AI development lifecycle and then monitor it in production. Starting in December, AI reports will be available in private preview in a US and EU Azure region for Azure AI Foundry customers. To request access to the private preview of AI reports, please complete the Interest Form. Furthermore, we are excited to announce new collaborations with Credo AI and Saidot to support customers’ end-to-end AI governance. By integrating the best of Azure AI with innovative and industry-leading AI governance solutions, we hope to provide our customers with choice and help empower greater cross-functional collaboration to align AI solutions with their own principles and regulatory requirements. Building on learnings at Microsoft Microsoft’s approach for governing generative AI applications builds on our Responsible AI Standard and the National Institute of Standards and Technology’s AI Risk Management Framework. This approach requires teams to map, measure, and manage risks for generative applications throughout their development cycle. A core asset of the first—and iterative—map phase is the Responsible AI Impact Assessment. These assessments help identify potential risks and their associated harms, as well as mitigations to address them. As development of an AI system progresses, additional iterations can help development teams document their progress in risk mitigation and allow experts to review the evaluations and mitigations and make further recommendations or requirements before products are launched. Post-deployment, these assessments become a source of truth for ongoing governance and audits, and help guide how to monitor the application in production. You can learn more about Microsoft’s approach to AI governance in our Responsible AI Transparency Report and find a Responsible AI Impact Assessment Guide and example template on our website. How AI reports support AI impact assessments and GenAIOps AI reports can help organizations govern their GenAI models and applications by making it easier for developers to provide the information needed for cross-functional teams to assess production-readiness throughout the GenAIOps lifecycle. Developers will be able to assemble key project details, such as the intended business use case, potential risks and harms, model card, model endpoint configuration, content safety filter settings, and evaluation results into a unified AI report from within their development environment. Teams can then publish these reports to a central dashboard in the Azure AI Foundry portal, where business leaders can track, review, update, and assess reports from across their organization. Users can also export AI reports in PDF and industry-standard SPDX 3.0 AI BOM formats, for integration into existing GRC workflows. These reports can then be used by the development team, their business leaders, and AI, data, and other risk professionals to determine if an AI model or application is fit for purpose and ready for production as part of their AI impact assessment processes. Being versioned assets, AI reports can also help organizations build a consistent bridge across experimentation, evaluation, and GenAIOps by documenting what metrics were evaluated, what will be monitored in production, and the thresholds that will be used to flag an issue for incident response. For even greater control, organizations can choose to implement a release gate or policy as part of their GenAIOps that validates whether an AI report has been reviewed and approved for production. Key benefits of these capabilities include: Observability: Provide cross-functional teams with a shared view of AI models and applications in development, in review, and in production, including how these projects perform in key quality and safety evaluations. Collaboration: Enable consistent information-sharing between GRC, development, and operational teams using a consistent and extensible AI report template, accelerating feedback loops and minimizing non-coding time for developers. Governance: Facilitate responsible AI development across the GenAIOps lifecycle, reinforcing consistent standards, practices, and accountability as projects evolve or expand over time. Build production-ready GenAI apps with Azure AI Foundry If you are interested in testing AI reports and providing feedback to the product team, please request access to the private preview by completing the Interest Form. Want to learn more about building trustworthy GenAI applications with Azure AI? Here’s more guidance and exciting announcements to support your GenAIOps and governance workflows from Microsoft Ignite: Learn about new GenAI evaluation capabilities in Azure AI Foundry Learn about new GenAI monitoring capabilities in Azure AI Foundry Learn about new IT governance capabilities in Azure AI Foundry Whether you’re joining in person or online, we can’t wait to see you at Microsoft Ignite 2024. We’ll share the latest from Azure AI and go deeper into capabilities that support trustworthy AI with these sessions: Keynote: Microsoft Ignite Keynote Breakout: Trustworthy AI: Future trends and best practices Breakout: Trustworthy AI: Advanced AI risk evaluation and mitigation Demo: Simulate, evaluate, and improve GenAI outputs with Azure AI Foundry Demo: Track and manage GenAI app risks with AI reports in Azure AI Foundry We’ll also be available for questions in the Connection Hub on Level 3, where you can find “ask the expert” stations for Azure AI and Trustworthy AI.
Continuously monitor your GenAI application with Azure AI Foundry and Azure Monitor
Now, Azure AI Foundry and Azure Monitor seamlessly integrate to enable ongoing, comprehensive monitoring of your GenAI application's performance from various perspectives, including token usage, operational metrics (e.g. latency and request count), and the quality and safety of generated outputs. With online evaluation, now available in public preview, you can continuously assess your application's outputs, regardless of its deployment or orchestration framework, using built-in or custom evaluation metrics. This approach can help organizations identify and address security, quality, and safety issues in both pre-production and post-production phases of the enterprise GenAIOps lifecycle. Additionally, online evaluations integrate seamlessly with new tracing capabilities in Azure AI Foundry, now available in public preview, as well as Azure Monitor Application Insights. Tying it all together, Azure Monitor enables you to create custom monitoring dashboards, visualize evaluation results over time, and set up alerts for advanced monitoring and incident response. Let’s dive into how all these monitoring capabilities fit together to help you be successful when building enterprise-ready GenAI applications. Observability and the enterprise GenAIOps lifecycle The generative AI operations (GenAIOps) lifecycle is a dynamic development process that spans all the way from ideation to operationalization. It involves choosing the right base model(s) for your application, testing and making changes to the flow, and deploying your application to production. Throughout this process, you can evaluate your application’s performance iteratively and continuously. This practice can help you identify and mitigate issues early and optimize performance as you go, helping ensure your application performs as expected. You can use the built-in evaluation capabilities in Azure AI Foundry, which now include remote evaluation and continuous online evaluation, to support end-to-end observability into your app’s performance throughout the GenAIOps lifecycle. Online evaluation can be used in many different application development scenarios, including: Automated testing of application variants. Integration into DevOps CI/CD pipelines. Regularly assessing an application’s responses for key quality metrics (e.g. groundedness, coherence, recall). Quickly responding to risky or inappropriate outputs that may arise during real-world use (e.g. containing violent, hateful, or sexual content) Production application monitoring and observability with Azure Monitor Application Insights. Now, let explore how you can use tracing for your application to begin your observability journey. Gain deeper insight into your GenAI application's processes with tracing Tracing enables comprehensive monitoring and deeper analysis of your GenAI application's execution. This functionality allows you to trace the process from input to output, review intermediate results, and measure execution times. Additionally, detailed logs for each function call in your workflow are accessible. You can inspect parameters, metrics, and outputs of each AI model utilized, which facilitates debugging and optimization of your application while providing deeper insights into the functioning and outputs of the AI models. The Azure AI Foundry SDK supports tracing to various endpoints, including local viewers, Azure AI Foundry, and Azure Monitor Application Insights. Learn more about new tracing capabilities in Azure AI Foundry. Continuously measure the quality and safety of generated outputs with online evaluation With online evaluation, now available in public preview, you can continuously evaluate your collected trace data for troubleshooting, monitoring, and debugging purposes. Online evaluation with Azure AI Foundry offers the following capabilities: Integration between Azure AI services and Azure Monitor Application Insights Monitor any deployed application, agnostic of deployment method or orchestration framework Support for trace data logged via the Azure AI Foundry SDK or a logging API of your choice Support for built-in and custom evaluation metrics via the Azure AI Foundry SDK Can be used to monitor your application during all stages of the GenAIOps lifecycle To get started with online evaluation, please review the documentation and code samples. Monitor your app in production with Azure AI Foundry and Azure Monitor Azure Monitor Application Insights excels in application performance monitoring (APM) for live web applications, providing many experiences to help enhance the performance, reliability, and quality of your applications. Once you’ve started collecting data for your GenAI application, you can access an out-of-the-box dashboard view to help you get started with monitoring key metrics for your application directly from your Azure AI project. Insights are surfaced to you via an Azure Monitor workbook that is linked to your Azure AI project, helping you quickly observe trends for key metrics, such as token consumption, user feedback, and evaluations. You can customize this workbook and add tiles for additional metrics or insights based on your business needs. You can also share it with your team so they can get the latest insights as well. Build enterprise-ready GenAI apps with Azure AI Foundry Ready to learn more? Here are other exciting announcements from Microsoft Ignite to support your GenAIOps workflows: New tracing and debugging capabilities to drive continuous improvement New ways to evaluate models and applications in pre-production New ways to document and share evaluation results with business stakeholders Whether you’re joining in person or online, we can’t wait to see you at Microsoft Ignite 2024. We’ll share the latest from Azure AI and go deeper into best practices for GenAIOps with these breakout sessions: Multi-agentic GenAIOps from prototype to production with dev tools Trustworthy AI: Advanced risk evaluation and mitigation Azure AI and the dev toolchain you need to infuse AI in all your appsDiscover the Future of Data Engineering with Microsoft Fabric for Technical Students & Entrepreneurs
Microsoft Fabric is an all-in-one analytics solution for enterprises that covers everything from data movement to data science, Real-Time Analytics, and business intelligence. It offers a comprehensive suite of services, including data lake, data engineering, and data integration, all in one place. This makes it an ideal platform for technical students and entrepreneurial developers looking to streamline their data engineering and analytics workflows.
