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Create a fully automated, end-to-end IRIS Training and Deployment using Azure MLOps

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Introduction

Fully Automated end-to-end Training and Deployment of IRIS Classifer using Azure MLOps

Prerequisites

  1. Azure [Account] (https://azure.microsoft.com/en-in/free/search/?&ef_id=EAIaIQobChMIhIHs3_Ca7wIVI4ZLBR0yKQsDEAAYASAAEgLhFvD_BwE:G:s&OCID=AID2100054_SEM_EAIaIQobChMIhIHs3_Ca7wIVI4ZLBR0yKQsDEAAYASAAEgLhFvD_BwE:G:s)
  2. Understanding of Azure DevOps

Getting Started

  1. Read more about Azure MLOps

Azure DevOps Instructions

  1. Create a Code Repo/Link git repos
  2. Create a Project named MLOpsIRIS
  3. Create a Service Connection ( This will be used in Build and Deploy pipelines). Project Settings --> Service Connections --> New Service Connections --> Azure Resource Manager

Build & Deploy Steps

  1. Build steps can be found in CLI Commands/Build direectory
  2. Deployment steps can be found in CLI Commands/Deploy direectory

Build and Deploy screenshots can be found in screenshots folder.

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Create a fully automated, end-to-end IRIS Training and Deployment using Azure MLOps

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  • Python 98.4%
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