https://github.com/patchy631/mlops-azureml
E2E CI/CD Pipeline of a machine learning project on Azure
https://github.com/patchy631/mlops-azureml
Last synced: 12 months ago
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E2E CI/CD Pipeline of a machine learning project on Azure
- Host: GitHub
- URL: https://github.com/patchy631/mlops-azureml
- Owner: patchy631
- Created: 2022-07-29T03:59:48.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-08-08T09:51:12.000Z (almost 4 years ago)
- Last Synced: 2025-06-27T18:11:59.081Z (12 months ago)
- Language: Python
- Size: 942 KB
- Stars: 7
- Watchers: 2
- Forks: 3
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Azure MLOps Talk
(https://www.youtube.com/watch?v=pLd7xF0z5Zs)
# 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](https://azure.microsoft.com/en-in/services/devops/)
# Getting Started
1. Read more about [Azure MLOps](https://azure.microsoft.com/en-in/services/machine-learning/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.