Probabilistic inference can reduce data pipeline engineering effort by orders of magnitude
Chris Ré · Chris Re · Jul 15, 2017 · at 3:08
Stanford Professor Chris Ré describes how DeepDive uses probabilistic inference to identify which parts of a data pipeline need work, yielding massive efficiency gains.
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“In contrast, we can build a pipeline first, which may not be very high quality, and then use probabilistic inference as a way to say, where should we spend our effort next? And the reduction in effort can sometimes be orders of magnitude, as we've seen in some of the applications we've built in our lab.”
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