Wine_Qauitiy_MLOPS_with_AWS_Deployment_With_CDCI Pipleine

Creating project from scratched to implement the MLOPs with Deployment on AWS

Workflows

1. Update config.yaml
2. Update schema.yaml #schema of your data
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/AM-Ankitgit/MLOPS_with_AWS_Deployment.git

STEP 01- Create a conda environment after opening the repository

conda create -n venv 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 url (this url is created by dagshub)

for windows user set url and you can see the dashboard of mlflow

set MLFLOW_TRACKING_URI=https://dagshub.com/AM-Ankitgit/MLOPS_with_AWS_Deployment.mlflow

set MLFLOW_TRACKING_USERNAME=AM-Ankitgit

set MLFLOW_TRACKING_PASSWORD=9a0cfdb8c9f9890d8d9e80455ae5918fcb9f4cb6

import dagshub
dagshub.init(repo_owner='AM-Ankitgit', repo_name='MLOPS_with_AWS_Deployment', mlflow=True)

# example script :

import mlflow
with mlflow.start_run():
  mlflow.log_param('parameter name', 'value')
  mlflow.log_metric('metric name', 1)

Run this to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/entbappy/End-to-end-Machine-Learning-Project-with-MLflow.mlflow

export MLFLOW_TRACKING_USERNAME=entbappy 

export MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbcef0

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

# Name of Policy:

1. AmazonEC2ContainerRegistryFullAccess

2. AmazonEC2FullAccess

3. Create ECR repo to store/save docker image

- Save the URI: 322848076327.dkr.ecr.us-east-1.amazonaws.com/mlproject

4. Create EC2 machine (Ubuntu)

5. Open EC2 and Install docker in EC2 Machine:

open the ec2 terminal
#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

# check your docker is installed
docker --version

6. Configure EC2 as self-hosted runner:

setting>actions>runner>new self hosted runner> choose os> then run command one by one
![alt text](image.png)

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 = mlproj
ECR_REPOSITORY_NAME diamond

About MLflow

MLflow

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