MLOps-Pipeline-Template-FakeNewsDetection

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MLOps-Pipeline-Template-FakeNewsDetection

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Model that classifies the news as Fake or Real and a complete MlOps Template for machine learning projects.

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# Fake News Detector + Comprehensive MLOps Pipeline Template for Machine Learning Projects
Enhance the efficiency and scalability of your machine learning projects with our MLOps Pipeline Template. This comprehensive guide will help you understand, customize, and efficiently utilize the template to align with your project requirements.

![image](https://github.com/Emanalytics7/MLOps-Pipeline-Template-FakeNewsDetection/assets/142586747/68db41fa-3a8f-4ed8-8561-c9d94c5afcd5)

## Welcome to the MLOps Pipeline Template
This template is designed to automate and streamline your machine learning projects, making them robust and scalable. Below, you'll find a comprehensive guide to utilizing and customizing this template to fit your specific needs.

*Make your own project super cool!* :)

Start from the basics [MlOps-Explained](https://www.arrikto.com/mlops-explained/)

# Prerequisites
## Skills Required

- **Python**: Proficiency in Python programming is essential as the project's codebase is primarily Python.
- **Basic Machine Learning Knowledge**: Understand fundamental machine learning concepts and workflows.

## Technical Setup
- **Development Environment**: You should have a setup suitable for Python development. I recommend using an IDE like PyCharm or VSCode.
- **Python Environment**: Know how to set up and manage Python environments using `venv` or `conda`.
- 
    ```bash
    python -m venv venv
    source venv/bin/activate  # On Windows use `venv\Scripts\activate`

- **Dependencies**:
     Ensure you know how to install dependencies from a requirements.txt file:
  
    ```bash
    pip install -r requirements.txt
    ```
    ---

# How it works
```sh
+---------------------------------------+
|             MLOps Pipeline            |
+---------------------------------------+
|                                       |
|   [Start]                             |
|       |                               |
|       v                               |
|   [Data Ingestion]                    |
|       |                               |
|       v                               |
|   [Data Preprocessing]                |
|       |                               |
|       v                               |
|   [Feature Engineering] -> [OPTIONAL] |
|       |                               |
|       v                               |
|   [Model Training]                    |
|       |                               |
|       v                               |
|   [Model Evaluation]                  |
|       |                               |
|       v                               |
|   [Model Deployment] -> [OPTIONAL]    |
|       |                               |
|       v                               |
|   [API Development]                   |
|       |                               |
|       v                               |
|   [Containerization with Docker]      |
|       |                               |
|       v                               |
|   [CI/CD Integration]                 |
|                                       |
+---------------------------------------+
```

# Project Overview
This template is structured to support end-to-end machine learning workflows, from data ingestion to model deployment. Here's how the project is organized:

``` sh
├── api
│   ├── api.py           # API for model serving
│   ├── model.pkl        # Serialized model file
│   └── vectorizer.pkl   # Serialized feature vectorizer
├── artifacts
│   ├── raw_data.zip     # Compressed dataset
├── config
│   └── settings.ini     # Configuration settings
├── notebooks
│   └── data_exploration.ipynb # Jupyter notebook for data analysis
├── src
│   ├── pipeline
│   │   ├── stage_01_ingestion.py    # Data ingestion script
│   │   ├── stage_02_preprocessing.py # Data preprocessing script
│   │   ├── stage_03_training.py      # Model training script
│   │   └── stage_04_evaluation.py    # Model evaluation script
│   └── utils
│       ├── config.py    # Configuration parser
│       └── logger.py    # Custom logger
├── .gitignore            # Specifies intentionally untracked files to ignore
├── README.md             # README file
├── Dockerfile            # Dockerfile for containerization
├── main.py               # Main script to run pipeline steps
├── requirements.txt      # Project dependencies
└── template.py           # Template script
```
*RUN THE `template.py` to get this project structure!*

# Getting Started
Before diving into the pipeline, I encourage you to explore your data and try different models using the `data_exploration.ipynb` notebook. It's essential to identify which model performs best for your specific use case.

## Utilities and Logging
To ensure high-quality code and maintainability, I've included a utilities module in src/utils/.

- `config.py`: A configuration parser utility that helps in dynamically reading your settings from `config/settings.ini`. It ensures that your scripts have access to up-to-date configurations without hardcoding values.
- `logger.py`: A custom logging utility that wraps around Python's built-in logging module. It provides a consistent logging interface for your application, making debugging and monitoring the pipeline's performance easier. You can track the execution flow, errors, and informative messages that help in maintaining a clean execution log for audit and debugging purposes.
To utilize the logger, simply import it in your scripts and use it to log messages at different severity levels

#### Customize Your Configuration
Set data sources and model parameters in `config/settings.ini`.
- Adapt `src/pipeline/stage_01_ingestion.py` for your data ingestion needs.
  
## Executing the Pipeline
Run the pipeline with these commands:
``` sh
python main.py ingest
python main.py preprocess
python main.py train
python main.py evaluate
```

## Tailoring the Pipeline Steps
Customize the scripts in `src/pipeline/` to match your machine learning tasks.

*I used Logistic Regression for Fake News Detection; feel free to adapt or choose a more complex model.*

## For API and Docker Deployment
- Tailor the `api/api.py` to set up your prediction endpoints.
- Adjust the `Dockerfile` to bundle your API and model into a container for deployment.

If you're unfamiliar with Docker or how to use it, start by reading this article: [How Docker Containers Work](https://www.freecodecamp.org/news/what-is-docker-used-for-a-docker-container-tutorial-for-beginners/), and then watch the video: [Build YOUR OWN Dockerfile](https://www.youtube.com/watch?v=SnSH8Ht3MIc).

## CI/CD Integration

Take advantage of the `.github/workflows` templates included in this project to set up Continuous Integration (CI) and Continuous Deployment (CD) with GitHub Actions and Azure. CI/CD helps automate steps in your software delivery process, such as initiating automatic builds and deployments.

For a deeper understanding of CI/CD principles and benefits, read the article: [Building an Effective CI/CD Pipeline: A Comprehensive Guide](https://gartsolutions.medium.com/building-an-effective-ci-cd-pipeline-a-comprehensive-guide-bb07343).

## Deployment on Azure using GitHub Actions

To get started with deploying on Azure, ensure you have an Azure subscription. If you're a student, you're in luck! You can sign up for the Azure for Students offer, which provides you with **$100** in Azure credits for the first year.

Here's a quick rundown:

1. Sign up for Azure if you haven't already. If you're a student, access your free credits through the [Azure for Students](https://azure.microsoft.com/en-us/free/students/) offer.
2. Once you have your subscription, log into the [Azure Portal](https://portal.azure.com/) and create a resource group.(Ask ChatGPT that how to create a resource group using azure portal or azure CLI)
3. With your environment set up, you'll use the Azure CLI for the next steps. If you don't have it installed, follow the instructions here:
[Install the Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli).

4. After setting up the CLI, use the `az login` command to log in to your Azure account.
5. Proceed with the rest of the required steps to configure your GitHub Actions for Azure deployment. A detailed tutorial is available here:
 [Deploying Docker to Azure App Service](https://docs.github.com/en/actions/deployment/deploying-to-your-cloud-provider/deploying-to-azure/deploying-docker-to-azure-app-service).
   
*This setup allows you to deploy any containerized application seamlessly using GitHub Actions directly to Azure App Service.*
![image](https://github.com/Emanalytics7/MLOps-Pipeline-Template-FakeNewsDetection/assets/142586747/14ee8bf2-bc26-4e8f-b845-3eb6c6436cb8)

## Integrating the Fake News Detection API

I have developed a Fake News Detection API that is ready to be integrated into your applications. You can easily leverage this API for real-time predictions by sending requests to the `/predict` endpoint. The model backing this API boasts a high F1 score of 0.98 on testing data, indicating its reliability and precision.

## Show Your Support

If this project has been beneficial to you, please consider giving it a star⭐ on GitHub! Your support encourages further development and helps others discover the project.

Thank you for exploring this MLOps pipeline template. It has been a valuable learning experience, and I hope it will be equally insightful for you. :)
---

### !Note
It can be significantly improved by you. While version control could elevate your project, I've avoided using it here to keep things simple. Additionally, more advanced technologies could be considered

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