Continuous Machine Learning (CML): An Open-Source CI/CD library for Machine Learning – MarkTechPost

Continuous Machine Learning (CML): An Open-Source CI/CD library for Machine Learning – MarkTechPost

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Image Source: https://cml.dev/#use-cases
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Totally, open-source, and made by the same development team that created the popular DVC (data version control) library, CML (Continous Machine Learning) library is a great tool that can be used to automate Machine learning workflows, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets. This brings the power of DevOps to ML or MLOps.

CML is built with the objective of bringing ML projects, and MLOps practices in a way such that it should be built on top of traditional engineering tools and not as a separate stack. This could be the future of MLOps.

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CML tool is built with the following principles:

  • GitFlow for data science. Use GitLab or GitHub to manage machine learning ML experiments, track who trained ML models or modified data, and when. Codify data and models with DVC instead of pushing to a Git repo.
  • Automated reports for ML experiments. Auto-generate reports with analytics in each Git Pull Request.
  • No additional services. Build your own ML platform using just GitHub or GitLab and your favorite cloud services: AWS, Azure, GCP. No databases, services, or complex setup needed.

Github: https://github.com/iterative/cml

Website: https://cml.dev/

Use Cases: https://cml.dev/#use-cases

https://cml.dev/

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Asif Razzaq is an AI Tech Blogger and Digital Health Business Strategist with robust medical device and biotech industry experience and an enviable portfolio in development of Health Apps, AI, and Data Science. An astute entrepreneur, Asif has distinguished himself as a startup management professional by successfully growing startups from launch phase into profitable businesses. This has earned him awards including, the SGPGI NCBL Young Biotechnology Entrepreneurs Award.

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