The Azure Naming terraform module provides a consistent way to generate resource names that adhere to established conventions in Azure deployments. This tool facilitates unique naming for resources, particularly useful for ensuring no conflicts in various subscriptions or regions, enhancing organization and efficiency in Terraform projects.
I have been testing out the Azure Verified Module for Azure Kubernetes Service that can be found in the Terraform Registry at https://registry.terraform.io/modules/Azure/avm-res-containerservice-managedcluster. This module came out in October 2024 so its fairly new. This module is suitable for enterprise-grade production environments, applies Microsoft best practices, features RBAC, complex monitoring. Also it is supported by …
Continue reading Exploring Azure Verified Module for Azure Kubernetes Service
I came across the Azure Verified Module for Azure Kubernetes Service and in its Github repo I found a Well Architected Framework (WAF) Aligned example for deploying this Terraform module. So asked myself, "What exactly makes this example of deploying AKS WAF Aligned?" Before I get into that, let me explain what is WAF. It …
Continue reading Well Architected Framework With The Azure Verified Module For Azure Kubernetes
Now that I have fine-tuned a model in Part 2, next is to deploy the fine tuned model into a new Kaito workspace. This blog post is part of a series.Part 1: Intro and overview of the KAITO fine-tuning workspace yamlPart 2: Executing the Training Kubernetes Training JobPart 3: Deploying the Fine-Tuned ModelPart 4: Evaluating …
I will continue from the Part 1 to execute the deployment of the fine-tuning workspace job. This blog post is part of a series.Part 1: Intro and overview of the KAITO fine-tuning workspace yamlPart 2: Executing the Training Kubernetes Training JobPart 3: Deploying the Fine-Tuned ModelPart 4: Evaluating the Fine-Tuned Model Let' start the fine …
Continue reading Deep Dive Into Fine-Tuning An LM Using KAITO on AKS – Part 2: Execution
I uncover the process and workings of fine-tuning a large language model on a dataset. Scenario for fine-tuning a model can be on corporate data such as a knowledge base, product information, human resources. I walk you through the process and share my detailed observations. What is Fine-tuning a large language model? Involves adjusting a …
Continue reading Deep Dive Into Fine-Tuning An LM Using KAITO on AKS – Part 1: Intro
I have been exploring some in depth tutorials in building a chat application. It implements Retrieval Augmentation Generation (RAG) on a product database in Azure AI Search. The scenario is for a retail customer to ask product recommendations on camping gear. This solution employs Azure AI Foundry for LLM models and interfacing. My goal of …
Continue reading Building a Chat App using Azure AI Foundry SDK and AI Search
The Azure AI Foundry portal is a cohesive environment for developing AI solutions, enabling users to create Hubs and Projects that facilitate collaboration among data scientists, developers, and engineers. It integrates services like Azure Open AI, offering shared resources and role-based access management while streamlining AI services' deployment and management processes.
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
I did an experiment of running a load test against 4 applications and while executing a Azure Kubernetes Cluster Upgrade to see the affects and any downtime. The applications hosted in my AKS cluster are AKS Helloworld - super simple application Voting App - simple application with Redis backend Bookinfo Istio demo - microservices architecture …
Roy Kim on Azure and AI 