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Shreya Shankar — Operationalizing Machine Learning
Gradient Dissent: Conversations on AI
The Impact of Corrupted Features in Production: A Concrete Story
In a humorous and relatable way, the speaker acknowledges the importance of firsthand experience in understanding a problem. They suggest telling a concrete story about how a feature getting corrupted in production can cause havoc. Though aware that people may criticize examples from big companies, they believe that the story illustrates the chain of events that can happen to any company. They proceed to give an example from their previous company where features from different sources and data pipelines led to failures that affected model performance. The speaker concludes by emphasizing that such issues frequently occur, including situations where clients send data regularly.
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