Machine Learning Best Practices For Your Company
Git and GitHub/GitLab Essentials
- Introduction to Git & GitHub/GitLab: For newcomers, I recommend the Udacity course "Version Control with Git". It provides a solid foundation.
- Configuring Git: Ensure your Git client is set up with your email and linked to your GitHub/GitLab account. This aids in tracking contributions and collaborations.
- Using Repositories: Utilize git-based repositories for all coding tasks. Ensure you have access to your company's GitLab or GitHub groups to create and manage repositories.
- Branching Strategy: Avoid direct pushes to the
mainbranch. Use feature branches, tags, and pull requests (PRs) for code reviews and merges.
- Pull Requests & Code Reviews: Always create Pull Requests for code reviews. If working solo, review your own PRs critically.
- Tools for Enhanced Experience: Employ GitHub Desktop for a GUI experience, and GitHub CLI and Hub CLI for command-line proficiency.
- Ignoring Files: Exclude unnecessary files like dependencies, build artifacts, and large files from Git tracking.
- Template for .gitignore: If unsure about what to ignore, use gitignore.io to generate a suitable
.gitignorefile for your project.
Commit Message Guidelines
- Writing Effective Commit Messages: Develop a habit of writing descriptive and meaningful commit messages. Refer to FreeCodeCamp's Guide and Chris Beams' Tips for best practices.
Handling Secret Keys
- Secrecy is Paramount: Never commit sensitive data like API keys or passwords. Use
.envfiles for environment variables and ensure they are listed in
- In Case of Exposure: If you accidentally commit sensitive data, inform your senior immediately and follow the steps to remove it from the repository as outlined here.
- Document Your Projects: Every repository should include a README.md file. Follow these best practices for creating effective READMEs.
- Automate with Git Hooks: Use Git hooks for automated scripts that run during events like commit, push, and receive. Learn more at Githooks.
- Data Version Control (DVC): Integrate DVC with Git for efficient data and model file management without bloating the Git repository.
Machine Learning Best Practices
- Continuous Machine Learning (CML): Adopt practices from CML for integrating ML workflows with software engineering best practices.
- Google's ML Best Practices: Explore Google's guidelines for insightful ML practices.
Jupyter Notebook Standards
- Reproducibility and Clean Code: Ensure notebooks are clean, modular, and reproducible. Favor JupyterLab over traditional notebooks and use templates for common tasks.
- Code Organization and TDD: Organize code in separate files and classes, and adopt Test-Driven Development (TDD) for your ML models. Read more about ML and TDD here.
- ML Flow for Experiment Management: Use tools like ML Flow to document, track, and compare experiments. Explore their tutorials for comprehensive guidance.
Modernizing ML Operations (MLOps)
- Incorporate LLMs into Workflows: Leverage Large Language Models (LLMs) like GPT-4 for automated code reviews, documentation generation, and even in generating initial code templates.
- AI-Assisted Coding: Tools like GitHub Copilot can significantly enhance coding efficiency by suggesting code snippets and improving overall development workflow.
- Deep Learning in CI/CD Pipelines: Integrate deep learning models into your CI/CD pipelines, ensuring models are tested and deployed efficiently.
- MLOps Best Practices: Stay updated with evolving MLOps practices by exploring resources like "ML Ops: Machine Learning as an Engineering Discipline".
- Cloud and Edge ML Deployments: Explore cloud-based ML services and edge computing for deploying models, ensuring scalability and accessibility.
- Ethical AI Considerations: Incorporate ethical AI practices, ensuring fairness, privacy, and transparency in your ML models.
Incorporating these modern practices into your workflow not only enhances efficiency and collaboration but also ensures that your projects are scalable, secure, and maintainable in the fast-evolving landscape of software development and machine learning.