📄️ AI Workloads Workshop
In this lab, you will learn all about deploying AI workloads on AKS Automatic cluster. We'll start by running a containerized AI workload locally then deploy it to an AKS Automatic cluster. AKS Automatic is a new offering of AKS that simplifies the deployment of Kubernetes clusters. In a matter of minutes, you will have an enterprise ready Kubernetes cluster that is ready to run your AI workloads securely and at scale.
📄️ Scaling AI Workloads with Ray
Learn how to deploy and scale distributed AI workloads using Ray on Azure Kubernetes Service (AKS). This lab covers Ray cluster setup, distributed machine learning training, and scaling AI inference workloads.
📄️ Build RAG applications with KAITO RAGEngine
Retrieval Augmented Generation (RAG) is a powerful technique that combines the strengths of large language models (LLMs) with external knowledge sources. This approach enables more accurate and contextually relevant responses by retrieving information from knowledge bases or databases and using it to augment the LLM's output.
📄️ Deploy AI Models with KAITO and Headlamp
Kubernetes AI Toolchain Operator (KAITO) is an open-source operator designed to automate AI/ML model inference and tuning workloads within Kubernetes clusters. It focuses on popular large models from Hugging Face such as Falcon, Phi-3, and more, while providing key capabilities including:
📄️ Migrate to AKS Automatic with GitHub Copilot for App Modernization
This workshop demonstrates how to migrate and modernize the iconic Spring Boot PetClinic application from local execution to Azure AKS Automatic. You'll experience the complete modernization journey using AI-powered tools such as GitHub Copilot app modernization and Containerization Assist MCP Server.