Think Different

To deploy model espcially LLM based application in Azure can be daunting task manually. We can automate the deployment pipeline with Kubeflow.  I am providing one example of an end-to-end machine learning deployment pipeline using Kubeflow on Azure. This example will cover setting up a Kubeflow pipeline, training a model, and deploying the model. Prerequisites: 1. Azure Account : You need an Azure account. 2. Azure Kubernetes Service (AKS) : You need a Kubernetes cluster. You can create an AKS cluster via the Azure portal or CLI. 3. Kubeflow : You need Kubeflow installed on your AKS cluster. Follow the [Kubeflow on Azure documentation](https://www.kubeflow.org/docs/azure/aks/) to set this up. Step 1: Setting Up the Environment First, ensure you have the Azure CLI and kubectl installed and configured. ```sh # Install Azure CLI curl -sL https://aka.ms/InstallAzureCLIDeb | sudo bash # Install kubectl az aks install-cli # Log in to Azure az login # Set the subscription (if you have mu...

photo by pixabay Here's a breakdown of the key differences between Kubeflow and Airflow , specifically in the context of machine learning pipelines, with a focus on Large Language Models (LLMs): Kubeflow vs. Airflow for ML Pipelines (LLMs): Core Focus: Kubeflow: Kubeflow is a dedicated platform for machine learning workflows. It provides a comprehensive toolkit for building, deploying, and managing end-to-end ML pipelines, including functionalities for experiment tracking, model training, and deployment. Airflow: Airflow is a general-purpose workflow orchestration platform. While not specifically designed for ML, it can be used to automate various tasks within an ML pipeline. Strengths for LLMs: Kubeflow: ML-centric features: Kubeflow offers built-in features specifically beneficial for LLMs, such as Kubeflow Pipelines for defining and managing complex training workflows, Kubeflow Notebook for interactive development, and KFServing for deploying trained models. Sca...