Reproducible ML Experimentation with Rubicon-ML

Reproducible ML Experimentation with Rubicon-ML

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Intro

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1 of 10

Intro

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Reproducible ML Experimentation with Rubicon-ML

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  1. 1 Intro
  2. 2 Building a model is an iterative process
  3. 3 Tracking this iteration is important to developers and stakeholders
  4. 4 Tracking this iteration across a team can be difficult
  5. 5 We built and open sourced rubicon-ml to help!
  6. 6 Logging locally as a developer leveraging Scikit-learn
  7. 7 Other logging backends with £aspec
  8. 8 Sharing and comparing experiments with intake.
  9. 9 Visualizing experiments with Dash & Plotly
  10. 10 Integrating rubicon-ml into ML workflows at Capital One

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