Machine Learning

3 Mins Read

Building a Robust Machine Learning Pipeline with CI/CD (open source)

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Introduction

Machine Learning (ML) pipelines streamline model development, training, and deployment by automating repetitive tasks. However, manually deploying ML models can be error-prone and time-consuming. Integrating Continuous Integration (CI) and Continuous Deployment (CD) ensures that updates are tested and deployed efficiently, improving reliability and scalability.

In this guide, we’ll build a complete ML pipeline from scratch, covering:

  • Understanding the problem
  • Data preprocessing
  • Model training and evaluation
  • Model deployment using Flask
  • Implementing CI/CD with automated testing
  • Deploying using Docker

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Use Case: Predicting Iris Flower Species

To demonstrate an end-to-end ML pipeline, we’ll develop an ML model to classify iris flowers into three species based on petal and sepal dimensions.

Dataset Overview

We use the well-known Iris dataset, which consists of:

  • Features: Sepal length, Sepal width, Petal length, Petal width
  • Target Variable: Species (Setosa, Versicolor, Virginica)

ML Model Development

Setting Up the Environment

To ensure reproducibility, install the necessary dependencies:

pip install numpy pandas scikit-learn flask pytest requests

Data Preprocessing and Model Training

Data preprocessing is crucial in ML pipelines to clean and transform raw data before feeding it into a model. Here, we load the dataset, encode categorical values, split it into training and testing sets, train a RandomForest model, and evaluate its accuracy.

Expected Model Performance

An accuracy score is printed, indicating the model’s performance. Example output:

Accuracy: 0.9667

Model Deployment with Flask

Why Use Flask?

Flask is a lightweight Python framework for building web applications and APIs. We use it to serve our trained ML model as a REST API.

Creating the API Server

Running the Server

python app.py

Testing the API

We can test the API using curl:

API Response

{“prediction”: 0}

Implementing CI/CD Pipeline

What is CI/CD?

Continuous Integration (CI) ensures that code changes are automatically tested before merging. Continuous Deployment (CD) ensures that successfully tested changes are automatically deployed. We implement this using GitHub Actions.

Setting Up CI with GitHub Actions

Create a .github/workflows/test.yml file:

Writing Unit Tests

Unit tests ensure that the API functions correctly.

Running Tests Locally

pytest test_app.py

Deployment with Docker

Why Use Docker?

Docker allows us to containerize our application, making it easy to deploy across different environments.

Creating a Dockerfile

Building and Running the Docker Container

Testing with Docker

Expected Output

{“prediction”: 0}

Conclusion

In this guide, we built an end-to-end ML pipeline that includes:

  • Model training and evaluation
  • API deployment using Flask
  • Automated testing with GitHub Actions
  • Containerization with Docker

With CI/CD automation, ML applications can be deployed seamlessly, ensuring efficiency and scalability.

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WRITTEN BY Priya Kanere

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