A Simple CI/CD Setup for ML Projects

This article provides insights on best practices for developing projects in Python, particularly focusing on integrating GitHub Actions, creating virtual environments, managing requirements, formatting code, running tests, and creating a Makefile. It emphasizes the importance of code quality and efficient project management. The writer encourages further exploration of these topics to enhance work quality.

 A Simple CI/CD Setup for ML Projects

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Apply best practices and learn to use GitHub Actions to build robust code

Introduction

Dealing with integrations, deployment, scalability, and other aspects of Machine Learning projects can be complex. In this article, I outline best practices for balancing code quality and implementation time, using Deepnote for collaborative data science projects.

Start Simple — Readme

Keep an up-to-date and visually appealing Readme file to ensure clarity for developers, salespeople, and project managers.

Use virtual environments, your laptop will be happy

Create virtual environments to isolate projects and manage dependencies, enhancing development efficiency.

Create a Requirements file, your colleagues will be happy

Ensure code reproducibility by maintaining a requirements.txt file with all installed libraries, enabling seamless collaboration.

Format your code with Black

Use Black to format code clearly and neatly, enhancing code readability and maintainability.

Analyse your code with PyLint

Utilize PyLint to automatically check for errors, enforce coding standards, and assess code quality, ensuring robustness.

Run Tests, make sure your code is working

Implement unit tests using PyTest to ensure code functionality and reliability, supporting a robust development process.

I am lazy, I’ll use a Makefile

Create a Makefile to simplify and automate routine tasks such as installing requirements, formatting code, and running tests.

Run Everything at every push with GitHub Actions

Automate the entire process using GitHub Actions, ensuring that code quality checks and tests are run automatically on every push to GitHub.

Final Thoughts

Implementing these best practices can significantly improve code quality and streamline the development process. For AI KPI management advice and insights into leveraging AI, connect with us at hello@itinai.com or stay tuned on our Telegram t.me/itinainews or Twitter @itinaicom.

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