9:30 ÖÖ - 10:30 ÖÖ (UTC)

In this episode, we’ll dive into embedding models—important tools for working with vector databases and large language models. These models convert text into numerical representations, making it easier to process and retrieve information efficiently. After covering the core concepts, we’ll apply them in practice by building a Retrieval-Augmented Generation (RAG) app using Small Language Models (SLMs) and a vector database. Learn more: https://aka.ms/Learn-Collection

9:30 ÖÖ - 10:30 ÖÖ (UTC)

In this episode, we’ll dig deeper into multi-modal capabilities of local and remote AI models and use visualization tools for better insights. We’ll also dive into multi-modal support in the AI Toolkit, showcasing how to process and analyze images alongside text. By the end, you’ll see how these capabilities come together to enhance powerful AI applications. Learn more: https://aka.ms/Learn-Collection

9:30 ÖÖ - 10:30 ÖÖ (UTC)

In this episode, we’ll explore how to evaluate AI models and choose the right one for your needs. We’ll cover key performance metrics, compare different models, and demonstrate testing strategies using features like Playground, Bulk Run, and automated evaluations. Whether you're experimenting with the latest models or transitioning to a new version, these evaluation techniques will help you make informed decisions with confidence. Learn more: https://aka.ms/Learn-Collection

9:30 ÖÖ - 10:30 ÖÖ (UTC)

In this episode, we’ll explore agents and agentic frameworks—systems that enable AI models to make decisions, take actions, and automate complex tasks. We’ll break down how these frameworks work, their practical applications, and how to build and integrate them into your projects. By the end, you’ll have a clear understanding of how to build and leverage AI agents effectively. We will explore how to use and build agentic frameworks using AI Toolkit. Learn more: https://aka.ms/Learn-Collection