Note:

This report was developed as part of Cohort 1. The project and repository were inspired by tutorials from Krish Naik on YouTube. A big thank you to Krish Naik for the valuable insights and guidance that made this project possible!

End to End MAchine Learning Project

Run from terminal:

docker build -t favidocker.azurecr.io/studentperformance:latest . (mstudentperformance is the web app name, it can be any name you want to give your docker image)

docker login favidocker.azurecr.io

docker push favidocker.azurecr.io/studentperformance:latest

Azure Deployment Guide:

## Azure Deployment:
Initiate the Azure deployment process to set up your machine learning application.

## Azure Registry Setup:
Establish the Azure Container Registry to streamline the storage and management of Docker container images.

## Docker Setup on Local Machine:
Configure Docker on your local machine to enable containerization of your machine learning application.

## Push Docker to Container Registry (Azure):
Push your Docker container to the Azure Container Registry, ensuring seamless accessibility for deployment.

## Create Azure Web App:
Generate an Azure Web App to host and deploy your machine learning application.

## Pull Container Registry into Web App:
Integrate the Azure Container Registry with the Azure Web App, facilitating the deployment process.

## Configure GitHub Actions for Deployment:
Utilize GitHub Actions to automate the deployment process, ensuring a smooth and efficient workflow.

This step-by-step guide provides a comprehensive walkthrough for deploying your machine learning application using Azure services, Docker, and GitHub Actions.