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