/ml-model-dockerize

Primary LanguagePython

ML Model Dockerization

This repository demonstrates how to containerize a machine learning (ML) model using Docker. It includes scripts to train a simple linear regression model and perform inference on new data using the trained model.

Usage

  1. Clone the Repository: 
    git clone <repo_url>
  2. Build Docker Image:
   docker build -t <image-name> .
  1. Run Docker Container for Training:
   docker run <image-name>

This command executes the training script (train.py) inside the Docker container, training the linear regression model and saving it as linear_regression_model.joblib.

  1. Run Docker Container for Inference:
   docker run <image-name> python inference.py

This command loads the trained model and performs inference on a sample input, saving the predictions to output.csv.

Project Structure:

  • train.py: Python script for training the linear regression model.
  • inference.py: Python script for performing inference using the trained model.
  • Dockerfile: Dockerfile for building the Docker image.
  • requirements.txt: List of Python dependencies required for the project.