Model Maintenance Insights
The conversation highlights the critical importance of retaining raw data and the challenges faced when attempting to retrain models without proper documentation. Emphasizing the need for transparency, predictions should come with detailed insights that help users understand the underlying factors influencing outcomes. Additionally, the discussion touches on feature drift, illustrating how evolving data can impact model performance and necessitate ongoing adjustments.In this clip
From this podcast
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SDS 433: Data Science Trends for 2021 — with Ben Taylor
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