mlops_with_mlflow

This project is on prediction of wine quality based on various factors like density, sulphates, citric acid content, etc. Pipelines are created for data ingestion, data validation, model trainer & evaluation. MLFlow was used to compare the models' losses. The Dockerfile created was deployed on AWS.

Workflow

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/ArionDas/mlops_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

dagshub

MLFLOW_TRACKING_URI=https://dagshub.com/ariondasad/mlops_with_mlflow.mlflow
MLFLOW_TRACKING_USERNAME=ariondasad
MLFLOW_TRACKING_PASSWORD=fd5c71aeae64e193aa121ff063551849ae516b25
python script.py

Run this to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/ariondasad/mlops_with_mlflow.mlflow

export MLFLOW_TRACKING_USERNAME=ariondasad 

export MLFLOW_TRACKING_PASSWORD=fd5c71aeae64e193aa121ff063551849ae516b25

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: 631951754288.dkr.ecr.ap-south-1.amazonaws.com/mlops_with_mlflow

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