4:30 de. - 5:30 de. (UTC)

Today, there are many Generative AI models to choose from: Large Language Models (LLMs), Small Language Models (SLMs), proprietary models, open models, models in the cloud, models on devices, text models, code models, image models, and multimodal models. In this session, we'll provide guidance to help you choose the right model to fit your needs, and tools you can use to evaluate and compare models for your specific tasks.

4:30 de. - 5:30 de. (UTC)

Learn how to build a production-ready Retrieval Augmented Generation (RAG) application to chat over your domain's data with Azure AI Search. Discover the benefits of hybrid search with semantic ranker to find both vector and keyword-based results amongst billions of documents. Explore different approaches to data ingestion, to extract, split, vectorize, and store your data in Azure AI Search. Consider multiple ways to evaluate your RAG app answers, to ensure high-quality responses. Finally, see how you can productionize your RAG app with efficient vector storage, private network deployment, and user authentication.

4:30 de. - 5:30 de. (UTC)

The Azure AI Agent Service allows you to build semi-autonomous software that can be given a goal and will work to achieve that goal without you knowing in advance exactly how it's going to do that or what steps it's going to take. This is a powerful tool for automating tasks and processes that are too complex to be easily automated with traditional software.

4:30 de. - 5:30 de. (UTC)

Explore how Azure AI Foundry seamlessly integrates with agents to create intelligent, responsive systems. We'll open up the session with Thermo Fisher who has created a multi-agent system to query across a vast store of disparate data. Marco and Mads will then walk you through the new Agent Service, offering practical examples and best practices for implementing AI agents in various business contexts.

4:30 de. - 5:30 de. (UTC)

Deploying AI-enabled applications involves an application or microservice interacting with a LLM inferencing endpoint. Microservices architectures and a cloud native approach is ideal for hosting your intelligent apps. This session demonstrates how you can use Kubernetes and cloud-native tools to reduce operational overhead in building and running intelligent apps.