From Open Source to Traditional ML with James Lamb - ML 138
Adventures in Machine Learning
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Leveraging Open Source Projects for Wide Use and Effective Maintenance
This chapter explores the process of accepting and developing open source projects, such as pandas and MLflow, highlighting their origins and the importance of frameworks with staying power. It discusses the benefits of open sourcing projects, such as receiving security patches and allowing organizations to focus on their core business. The chapter also emphasizes the role of automated feedback and reducing friction in software development.
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