Romila Pradhan, Reasoning About Interventions in Data and Machine Learning Systems
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Modern machine learning systems are only as reliable as the data and pipelines that support them, yet improving their behavior often requires navigating enormous spaces of possible data and system configurations. These systems are complex enough that improving them by changing everything at once is both inefficient and unreliable. This talk explores a simple principle for building smarter data and machine learning systems: identify which intervention is most likely to change the outcome, and act there. I will first introduce DataSift that applies this principle to model behavior, identifying…
- Length
- 54:41
