Azure Infrastructure Workloads
What’s new
- Added Microsoft’s Quantum Ready program
Deep links
- Microsoft’s Quantum Ready program: Join Microsoft’s Quantum Ready program to build a robust strategy for quantum computing. (Source: Quantum Ready)
Visit the extended Partner Crucible page on this Topic Page for more partner resources.
Data AI Workloads - Azure OpenAI
What’s New
- Added Using Azure AI Agent Service with AutoGen / Semantic Kernel to build a multi-agent’s solution 🤖
- Added Empowering AI Agents with Tools via OpenAPI: A Hands-On Guide with Microsoft Semantic Kernel Agents 🔌
- Added Gartner Top GenAI Opportunities for Tech Products 📈
Deep Links
- Using Azure AI Agent Service with AutoGen / Semantic Kernel to build a multi-agent’s solution: Azure AI Agent Service is in the Public Preview stage. Using Azure AI Foundry SDK, developers can quickly build agents based on Azure AI Agent Service using Python or C#. Enterprises will have different AI Agents based on their business, so how should these AI Agents be combined in the workflow? We need to use AutoGen or Semantic Kernel to orchestrate these three AI Agents (Tech Community)
- Empowering AI Agents with Tools via OpenAPI: A Hands-On Guide with Microsoft Semantic Kernel Agents: This blog post showcases how to create a chat-based application for an e-commerce platform that manages customer payments through the integration of their existing Payment Service APIs (Dev Blogs)
- Gartner Top GenAI Opportunities for Tech Products: eBook: The top GenAI opportunities for tech products and 4 key actions to drive growth and enhance customer value (Gartner)
Visit the extended Partner Crucible page on this Topic Page for more partner resources.
Data AI Workloads
What’s new
- Added Azure AI Templates 🌐
- Added Lessons from red teaming 100 generative AI products 🧠
- Added Well-Architected AI workloads on Azure 💻
Deep links
- Azure AI Templates: Get started with AI application templates. Edit and deploy to Azure using VS Code or GitHub Codespaces. (GitHub)
- Lessons from red teaming 100 generative AI products: Based on our experience red teaming over 100 generative AI products at Microsoft, we present our internal threat model ontology and eight main lessons we have learned. (Microsoft AI Red Team)
- Well-Architected AI workloads on Azure: This article addresses architectural challenges of designing AI workloads. It focuses on nondeterministic functionality, data and application design, and operations. The recommendations are based on Azure Well-Architected Framework principles and include insights from successful Azure implementations. (Microsoft Learn)
Visit the extended Partner Crucible page on this Topic Page for more partner resources.
Digital Apps and Innovation Workloads
What’s new
- *Added a library of useful resources about Azure API Management 🚀
- Added Comprehensive AI Gateway 🧠
Deep links
- APIM Love: A library of useful resources about Azure API Management (aka.ms/apimlove) - APIM Love
- AI Gateway: A framework for confident deployment of Intelligent Apps into production - GitHub
Visit the extended Partner Crucible page on this Topic Page for more partner resources.