[D] How to maintain ML models?

This page summarizes the projects mentioned and recommended in the original post on reddit.com/r/MachineLearning

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  • dvc

    🦉Data Version Control | Git for Data & Models | ML Experiments Management

    DVC is a useful (git for data & models) https://dvc.org/ tool.

  • MLflow

    Open source platform for the machine learning lifecycle

    MLflow is a MLOps tool that may help you: https://mlflow.org/

  • OPS

    OPS - Build and Run Open Source Unikernels. Quickly and easily build and deploy open source unikernels in tens of seconds. Deploy in any language to any cloud.

  • MLOps

    MLOps examples

    Maybe something like this: https://github.com/microsoft/MLOps

  • awesome-seml

    A curated list of articles that cover the software engineering best practices for building machine learning applications.

    They also have an awesome-seml repo on GitHub outlining many (scientific) articles as well as tools and frameworks that may help you out in implementing these best practices.

  • mllint

    `mllint` is a command-line utility to evaluate the technical quality of Python Machine Learning (ML) projects by means of static analysis of the project's repository.

    Finally, there is the mllint tool that I have been developing during my MSc thesis on Software Quality in ML projects. While still a research prototype, it can already analyse your project and may be able to provide you with practical recommendations on what tools & techniques to employ for several aspects of your ML project's development. Feel free to try it out on your project and let me know what you think of it!

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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