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Are your experiments reproducible?
Today we're speaking with Luigi Quaranta about the state of reproducibility in machine learning.
Luigi published a taxonomy of support for reproducibility by various tools in the space and together we’re exploring the need for reproducibility, challenges and limitations, how to evaluate opportunities for improving your current systems, and what the future might hold.
A few papers would be relevant here.
The first is A Taxonomy of Tools for Reproducible Machine
Learning Experiments: http://ceur-ws.org/Vol-3078/paper-81.pdf
and the second is a study of how to make Jupyter notebook code of high quality, Eliciting Best Practices for Collaboration with Computational Notebooks: https://dl.acm.org/doi/abs/10.1145/3512934.
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By AutoML MediaToday we're speaking with Luigi Quaranta about the state of reproducibility in machine learning.
Luigi published a taxonomy of support for reproducibility by various tools in the space and together we’re exploring the need for reproducibility, challenges and limitations, how to evaluate opportunities for improving your current systems, and what the future might hold.
A few papers would be relevant here.
The first is A Taxonomy of Tools for Reproducible Machine
Learning Experiments: http://ceur-ws.org/Vol-3078/paper-81.pdf
and the second is a study of how to make Jupyter notebook code of high quality, Eliciting Best Practices for Collaboration with Computational Notebooks: https://dl.acm.org/doi/abs/10.1145/3512934.