Credit_Churn_Prediction

End to End Application Machine Learning Project using MLFlow and deployed in the AWS EC2 instance using github action

Workflows

Update config.yaml Update schema.yaml Update params.yaml Update the entity Update the configuration manager in src config Update the components Update the pipeline Update the main.py Update the app.py

How to run?

STEPS:

Clone the repository

https://github.com/sriramsripada20s/Credit_Churn_Prediction_with_MLFlow.git

STEP 01- Create a conda environment after opening the repository

conda create -n mlproj python=3.8 -y

conda activate mlproj

STEP 02- install the requirements

pip install -r requirements.txt

# Finally run the following command
python app.py

Now,

open up you local host and port

MLflow

Documentation

cmd

mlflow ui

dagshub

MLFLOW_TRACKING_URI=https://dagshub.com/sriramsripada20s/Credit_Churn_Prediction_with_MLFlow.mlflow
MLFLOW_TRACKING_USERNAME=sriramsripada20s
MLFLOW_TRACKING_PASSWORD=ae66b17586be4c00c9a087b1810f990fcac318c9
python script.py

Run this to export as env variables:

set MLFLOW_TRACKING_URI=https://dagshub.com/sriramsripada20s/Credit_Churn_Prediction_with_MLFlow.mlflow 

set MLFLOW_TRACKING_USERNAME=sriramsripada20s

set MLFLOW_TRACKING_PASSWORD=ae66b17586be4c00c9a087b1810f990fcac318c9

How to Deploy Streamlit app on EC2 instance

1. Login with your AWS console and launch an EC2 instance

2. Run the following commands

Note: Do the port mapping to this port:- 8501

sudo apt update

sudo apt-get update

sudo apt upgrade -y

sudo apt install git curl unzip tar make sudo vim wget -y

sudo apt install git curl unzip tar make sudo vim wget -y

git clone "Your-repository"

sudo apt install python3-pip

pip3 install -r requirements.txt

#Temporary running
python3 -m streamlit run app.py

#Permanent running
nohup python3 -m streamlit run app.py

Note: Streamlit runs on this port: 8501

AWS-CICD-Deployment-with-Github-Actions

1. Login to AWS console.

2. Create IAM user for deployment

#with specific access

1. EC2 access : It is virtual machine

2. ECR: Elastic Container registry to save your docker image in aws


#Description: About the deployment

1. Build docker image of the source code

2. Push your docker image to ECR

3. Launch Your EC2 

4. Pull Your image from ECR in EC2

5. Lauch your docker image in EC2

#Policy:

1. AmazonEC2ContainerRegistryFullAccess

2. AmazonEC2FullAccess

3. Create ECR repo to store/save docker image

- Save the URI: 566373416292.dkr.ecr.ap-south-1.amazonaws.com/mlproj

4. Create EC2 machine (Ubuntu)

5. Open EC2 and Install docker in EC2 Machine:

#optinal

sudo apt-get update -y

sudo apt-get upgrade

#required

curl -fsSL https://get.docker.com -o get-docker.sh

sudo sh get-docker.sh

sudo usermod -aG docker ubuntu

newgrp docker

6. Configure EC2 as self-hosted runner:

setting>actions>runner>new self hosted runner> choose os> then run command one by one

7. Setup github secrets:

AWS_ACCESS_KEY_ID=

AWS_SECRET_ACCESS_KEY=

AWS_REGION = us-east-1

AWS_ECR_LOGIN_URI = demo>>  566373416292.dkr.ecr.ap-south-1.amazonaws.com

ECR_REPOSITORY_NAME = simple-app

About MLflow

MLflow

Its Production Grade
Trace all of your expriements
Logging & tagging your model

sriramsripada20s / credit_churn_prediction_with_mlflow Goto Github PK

credit_churn_prediction_with_mlflow's Introduction

Credit_Churn_Prediction

Workflows

How to run?

STEPS:

STEP 01- Create a conda environment after opening the repository

STEP 02- install the requirements

MLflow

cmd

dagshub

How to Deploy Streamlit app on EC2 instance

1. Login with your AWS console and launch an EC2 instance

2. Run the following commands

Note: Do the port mapping to this port:- 8501

AWS-CICD-Deployment-with-Github-Actions

1. Login to AWS console.

2. Create IAM user for deployment

3. Create ECR repo to store/save docker image

4. Create EC2 machine (Ubuntu)

5. Open EC2 and Install docker in EC2 Machine:

6. Configure EC2 as self-hosted runner:

7. Setup github secrets:

About MLflow

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