Reproducibility and Experiment Tracking
Daniel and Lukas discuss the importance of reproducibility in machine learning research and the challenges of tracking experiments. They highlight the value of passive tracking and the need for clear documentation of input and output parameters. The conversation also touches on other tools and products they have developed to address pain points in the field.In this clip
From this podcast
The Gradient
Lukas Biewald: Crowdsourcing at CrowdFlower and ML Tooling at Weights & Biases
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