Machine-Learning-Project-with-MLflow

End-To-End Machine Learning Project with MLflow

Workflows

1. Update config.yaml
2. Update schema.yaml
3. Update params.yaml
4. Update the entity
5. Update the configuration manager in src config
6. Update the components
7. Update the pipeline
8. Update the main.py
9. Update the app.py

How to run?

Steps:

Clone the repository

https://github.com/saikrishnabudi/Machine-Learning-Project-with-MLflow.git

Step 01- Create a conda environment after opening the repository

conda create -n mlproject python=3.12 -y

conda activate mlproject

Step 02- install the requirements

pip install -r requirements.txt

# Run the app.py
python app.py

Now, open up the local host and port

ML Flow

[Documentation](https://mlflow.org/docs/latest/index.html)

cmd

- mlflow ui

dagshub

[dagshub](https://dagshub.com/dashboard)

MLFLOW_TRACKING_URI=https://dagshub.com/saikrishnabudi/Machine-Learning-Project-with-MLflow.mlflow \
MLFLOW_TRACKING_USERNAME=saikrishnabudi \
MLFLOW_TRACKING_PASSWORD=3efda87a7165edecfd46af3e63ed69f73a2d4801 \
python script.py

Run this to expert as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/saikrishnabudi/Machine-Learning-Project-with-MLflow.mlflow

export MLFLOW_TRACKING_USERNAME=saikrishnabudi

export MLFLOW_TRACKING_PASSWORD=3efda87a7165edecfd46af3e63ed69f73a2d4801

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: 264413606393.dkr.ecr.ap-south-1.amazonaws.com/mlproject

4. Create EC2 machine (Ubuntu)

5. Open EC2 and Install docker in EC2 Machine:

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

Docker Version

Docker version 26.0.2, build 3c863ff

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 = ap-south-1

AWS_ECR_LOGIN_URI = demo>>  264413606393.dkr.ecr.ap-south-1.amazonaws.com

ECR_REPOSITORY_NAME = mlproject