Wine Quality Prediction with MLFLOW experiments

Predicting wine quality using machine learning techniques and managing the end-to-end workflow with MLFLOW experiments on Dagshub, Deployment via Docker image on AWS EC2

Introduction

This project aims to predict the quality of wines using various machine learning algorithms. It utilizes the MLflow platform to manage the end-to-end machine learning lifecycle, including data preprocessing, model training, hyperparameter tuning, and deployment.

Features

• Data preprocessing pipeline for cleaning and transforming the dataset.
• Support for multiple machine learning algorithms for wine quality prediction.
• Hyperparameter tuning using grid search or random search.
• Tracking and logging experiments with MLflow for easy comparison.
• REST API endpoint for making predictions using the trained model.
• Dockerized environment for seamless deployment.

Project Structure

├── data/
│   ├── wine-quality.csv
│   └── ...
├── models/
│   ├── model.pkl
│   └── ...
├── notebooks/
│   ├── data_exploration.ipynb
│   ├── model_experimentation.ipynb
│   └── ...
├── .gitignore
├── Dockerfile
├── preprocess_data.py
├── train.py
├── predict_api.py
└── README.md

Getting Started

STEPS:

Clone the repository

https://github.com/tushar2704/Wine_Quality

STEP 01- Create a conda environment after opening the repository

conda create -n ml python=3.11 -y

conda activate ml

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/tushar27/Wine_Quality.mlflow
MLFLOW_TRACKING_USERNAME=tushar27
MLFLOW_TRACKING_PASSWORD=31e97a76110fac92de36585f12fb7c9ce02ea9f20
python main.py

Run this to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/tushar27/Wine_Quality.mlfloww

export MLFLOW_TRACKING_USERNAME=tushar27 

export MLFLOW_TRACKING_PASSWORD=31e97a76110fac92de36585f12fb7c9ce02ea9f20

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

Contact Information

If you have any questions, feedback, or collaboration opportunities, please feel free to reach out to me. You can contact me via email at info@tushar-aggarwal.com or connect with me on LinkedIn at Tushar Aggarwal.

Thank you for visiting my Data Analysis Portfolio! I hope you find my projects informative and insightful.

Author

• ©2023 Tushar Aggarwal. All rights reserved
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