The post outlines the installation process for the RAG Engine, detailing prerequisites like AKS and GPU provisioning. It guides users through creating an AKS cluster, installing the KAITO Workspace for model inference, and configuring the RAG Engine with a specific model. Finally, it demonstrates indexing documents and querying with RAG Engine.
The Kubernetes AI Toolchaining Operator (AKS) features a RAG engine that enables users to interact with private documents using a hosted language model, like Phi-4. This tool allows for grounded AI responses by indexing and retrieving relevant data. This is an AI platform offering management control and scalability supporting many Gen AI applications.
The blog discusses the Kubernetes AI Toolchain Operator (KAITO) setup on AKS, detailing the NVIDIA GPU VM instance types for node pools hosting AI inference models. It emphasizes cost efficiency and GPU specifications for deploying large models.
KAITO simplifies the deployment of large language models (LLMs) in Azure Kubernetes Service (AKS) environments with preset GPU configurations. This tool automates the setup process, including node provisioning and identity management, essential for data experiments while ensuring security compliance. It enhances efficiency, allowing engineers to focus on AI/ML model experimentation. #azure #kubernetes #AI #genAI #mvpbuzz
Roy Kim on Azure and AI 