title	description
Deployment Model Inference	A description of the project.

Deployment Model Inference

This project demonstrates a Dockerized inference pipeline for deploying a machine learning model. The pipeline loads a trained model, preprocesses input data, and makes predictions using the model.

Table of Contents

• Project Overview
• Prerequisites
• Installation
• Usage
• Docker Deployment
• Testing
• Contributing
• License

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Project Overview

The project consists of the following components:

• src/inference.py: The main script that loads the model, preprocesses input data, and makes predictions.
• src/model_loader.py: A utility script to load the trained model.
• src/data_preprocessor.py: A utility script to preprocess input data.
• models/trained_model_2025-01-02.joblib: A trained model saved in the models/ directory.
• Dockerfile: A Dockerfile to containerize the inference pipeline.
• pyproject.toml: A Poetry configuration file for dependency management.

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Prerequisites

Before running the project, ensure you have the following installed:

• Docker: Install Docker
• Poetry: Install Poetry

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Installation

1. Clone the repository:

   git clone https://github.com/iair/deployment_model_inference.git
   cd deployment_model_inference

2. Install dependencies using Poetry:

   poetry install

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Usage

Running Locally

1. Activate the Poetry virtual environment:

   poetry shell

2. Run the inference script:

   poetry run python src/inference.py

Input Data

The script expects input data in the following format:

input_data = {
    "Pregnancies": 6,
    "Glucose": 148,
    "BloodPressure": 72,
    "SkinThickness": 0,
    "Insulin": 0,
    "BMI": 33.6,
    "DiabetesPedigreeFunction": 0.627,
    "Age": 50
}

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Docker Deployment

Build the Docker Image

To build the Docker image, run:

docker build -t deployment_model_inference .

Run the Docker Container

To run the container and execute the inference script:

docker run -it deployment_model_inference:latest

Mount Local Files (Optional)

If you want to test changes to your code or model without rebuilding the Docker image, mount your local project directory into the container:

docker run -it -v $(pwd):/app deployment_model_inference:latest

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Testing

To test the inference pipeline, ensure the model file (models/trained_model_2025-01-02.joblib) exists and is correctly loaded. You can also test with different input data to verify the pipeline's robustness.

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Contributing

Contributions are welcome! If you'd like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/YourFeatureName).
3. Commit your changes (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/YourFeatureName).
5. Open a pull request.

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License

This project is licensed under the Apache 2.0 License. See the LICENSE file for details.

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Acknowledgments

• Thanks to the open-source community for providing tools and libraries that made this project possible.
• Special thanks to Poetry and Docker for simplifying dependency management and deployment.

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Contact

For questions or feedback, feel free to reach out:

• Author: Iair
• Email: [email protected]
• GitHub: iair

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How to Use

1. Open your project in Visual Studio Code.
2. Open the README.md file (or create one if it doesn’t exist).
3. Copy and paste the above content into the file.
4. Save the file (Ctrl + S or Cmd + S).

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