Pinning Dependencies the Right Way for ML Projects

📰 Medium · DevOps

Learn how to pin dependencies correctly for ML projects to avoid version drift issues and ensure reproducibility

intermediate Published 25 Jun 2026

Action Steps

Use pip-compile to generate a requirements.txt file with pinned dependencies
Specify exact versions of libraries in your requirements.txt file
Use a virtual environment to isolate dependencies and ensure reproducibility
Regularly update and re-pin dependencies to ensure compatibility
Test your project with different dependency versions to ensure robustness

Who Needs to Know This

Data scientists and ML engineers can benefit from this knowledge to ensure their projects are reliable and reproducible

Key Insight

💡 Pinning dependencies is essential for ML projects because loose dependency ranges can lead to silent breaks in compiled, binary-level contracts

Key Takeaways

Learn how to pin dependencies correctly for ML projects to avoid version drift issues and ensure reproducibility

Full Article

Title: Pinning Dependencies the Right Way for ML Projects

URL Source: https://medium.com/@sundarbalamurugan/pinning-dependencies-the-right-way-for-ml-projects-b35c82042d44?source=rss------devops-5

Published Time: 2026-06-25T06:57:45Z

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# **Pinning Dependencies the Right Way for ML Projects**

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[Sundar Balamurugan](https://medium.com/@sundarbalamurugan?source=post_page---byline--b35c82042d44---------------------------------------)

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## Why ML is different

In ordinary web projects, loose dependency ranges are usually fine. In machine learning, they are dangerous, because the libraries are bound together by compiled, binary-level contracts that break silently when versions drift apart. signspell taught me this the hard way.

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Pinning Dependencies the Right Way for ML Projects

Key Takeaways

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