The Future of AI: Customizing AI agents with the Semantic Kernel agent framework
The blog post Customizing AI agents with the Semantic Kernel agent framework discusses the capabilities of the Semantic Kernel SDK, an open-source tool developed by Microsoft for creating AI agents and multi-agent systems. It highlights the benefits of using single-purpose agents within a multi-agent system to achieve more complex workflows with improved efficiency. The Semantic Kernel SDK offers features like telemetry, hooks, and filters to ensure secure and responsible AI solutions, making it a versatile tool for both simple and complex AI projects.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.The Future of AI: Power Your Agents with Azure Logic Apps
Building intelligent applications no longer requires complex coding. With advancements in technology, you can now create agents using cloud-based tools to automate workflows, connect to various services, and integrate business processes across hybrid environments without writing any code.Automate Quota Discovery in Azure AI Foundry: A Tale of 3 APIs
Automate the discovery of Azure regions that meet your AI deployment needs using three essential APIs: Models API, Usages API, and Locations API. This process helps reduce decision fatigue and ensures compliance with enterprise-wide model deployment standards. Key learnings: Model Deployment Requirements: Understand the needs of a standard Retrieval-Augmented Generation (RAG) application, which involves deploying multiple models. Automation Benefits: Streamline your deployment process and ensure compliance with enterprise standards. Three Essential APIs: Models API: Query available models for a specific subscription within a chosen location. Usages API: Assess current usages and limits to infer available quotas. Locations API: Obtain a list of all available regions. A comprehensive Jupyter notebook with the implementation steps is available in the accompanying GitHub repository. This resource is invaluable for AI developers looking to streamline their deployment processes and ensure their applications meet all necessary requirementsThe Future of AI Is: Model Choice - From Structured Process To Seamless Platform
Language models are at the heart of generative AI applications. But in just over a year, we've moved from a handful of model providers to 1M+ community variants and more, resulting in the paradox of choice that ends in decision fatigue. In this blog post, we'll look at how developers can rethink their model selection strategy with a structured decision-making process, and a seamless development platform, to help them. This post is part of the Future of AI series jumpstarted by Marco Casalaina with his post on Exploring Multi-Agent AI Systems.Azure AI Foundry: Empowering Scientific Discovery with AI
Azure AI Foundry is enabling scientific discovery with the introduction of three groundbreaking models from Microsoft Research: Aurora, MatterSim, and TamGen. These models, available starting January 20, 2025, offer transformative capabilities in weather forecasting, materials simulation, and drug design. By providing access to these advanced tools, Azure AI Foundry is enabling researchers and developers to explore new frontiers and accelerate the pace of innovation.The Future of AI: Horses for Courses - Task-Specific Models and Content Understanding
Task-specific models are designed to excel at specific use cases, offering highly specialized solutions that can be more efficient and cost-effective than general-purpose models. These models are optimized for particular tasks, resulting in faster performance and lower latency, and they often do not require prompt engineering or fine-tuning.New controls for model governance and secure access to on-premises or custom VNET resources
Learn how to create an allowed model list for the Azure AI model catalog, plus a new way to access on-premises and custom VNET resources from your managed VNET for your training, fine-tuning, and inferencing scenarios.Announcing Healthcare AI Models in Azure AI Model Catalog
Modern medicine encompasses various data modalities, including medical imaging, genomics, clinical records, and other structured and unstructured data sources. Understanding the intricacies of this multimodal environment, Azure AI onboards specialized healthcare AI models that go beyond traditional text-based applications, providing robust solutions tailored to healthcare's unique challenges.Meta’s next generation model, Llama 3.1 405B is now available on Azure AI
Microsoft, in collaboration with Meta, is launching Llama 3.1 405B, now available via Azure AI’s Models as a Service. Also introducing fine-tuned versions of Llama 3.1 8B and 70B. Leverage powerful AI for synthetic data generation and distillation. Access these models and more through Azure AI Studio and popular developer tools like prompt flow, OpenAI, LangChain, LiteLLM, and more. Streamline development and enhance efficiency with Azure AI.
