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Experimentación ágil de machine learning con DVC

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Conforme aumenta el uso de modelos de machine learning en las empresas, y se requiere continuamente estarlos enriqueciendo y generando experimentos para estimar el impacto de los cambios, requerimos de herramientas que nos apoyen para facilitar, automatizar y gestionar estas tareas.
Presentado por: Ramón Valles

Published in: Technology
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Experimentación ágil de machine learning con DVC

  1. 1. Version Control for Machine Learning Projects @mroutis
  2. 2. About me - Name: Ramón Valles - Handle: mroutis - Work: Software developer - Company: iterative.ai @mroutis
  3. 3. Iterative AI Time & Effort @mroutis (Data Version Control)
  4. 4. Why versioning is important? @mroutis
  5. 5. Datasets evolve over time @mroutis
  6. 6. Different models @mroutis https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html
  7. 7. “Yea, we have version control” @mroutis https://www.reddit.com/r/ProgrammerHumor/comments/63rcvc/yea_we_have_version_control/
  8. 8. How do we identify each version? @mroutis
  9. 9. https://www.reddit.com/r/ProgrammerHumor/comments/99a9k8/version_control @mroutis
  10. 10. Manual work is error prone. @mroutis
  11. 11. Is versioning data / models enough? @mroutis
  12. 12. Reproducibility Crisis “I was recently chatting to a friend whose startup’s machine learning models were so disorganized... training the same model get different results!” - Peter Warden (2018) https://petewarden.com/2018/03/19/the-machine-learning-reproducibility-crisis/ @mroutis
  13. 13. @mroutis
  14. 14. What have been done so far to address this? @mroutis
  15. 15. @mroutis Tracking experiments
  16. 16. Manual work is error prone. @mroutis
  17. 17. @mroutis Pipelines
  18. 18. - Chain processes together - Reproduce in the correct order - Make it easier to return to old work - The output of one stage is the input of the next on @mroutis Pipelines
  19. 19. How does DVC address this problems? @mroutis
  20. 20. @mroutis
  21. 21. @mroutis Versioning with DVC
  22. 22. @mroutis Pipelines with DVC
  23. 23. @mroutis
  24. 24. @mroutis Experiment Tracking with DVC
  25. 25. @mroutis Reproduce experiments
  26. 26. @mroutis Collaborate
  27. 27. Want to learn more? - Check out docs: https://dvc.org/doc - Chat with us: https://dvc.org/chat - Contribute on GitHub: https://github.com/iterative/dvc - Follow us on Twitter: @DVCorg @mroutis
  28. 28. Thanks for listening! @mroutis Thanks for Listening - Chris Thile
  29. 29. Thanks for the invitation!

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