Dataiku
OCTOBER 16, 2020
For designing machine learning (ML) models as well as for monitoring them in production, uncertainty estimation on predictions is a critical asset. It helps identify suspicious samples during model training in addition to detecting out-of-distribution samples at inference time.
The Unofficial Google Data Science Blog
DECEMBER 14, 2021
by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature.
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Jedox
APRIL 9, 2020
Times of crisis mean uncertainty, both personally and professionally. In this blog post, we’ll look at the importance of agility in FP&A in being able to better manage uncertainties, even during uncertain times and build resilience for the future. Business Continuity. Better agility increases resiliency.
The Unofficial Google Data Science Blog
JULY 18, 2023
E ven after we account for disagreement, human ratings may not measure exactly what we want to measure. Researchers and practitioners have been using human-labeled data for many years, trying to understand all sorts of abstract concepts that we could not measure otherwise. That’s the focus of this blog post.
Rocket-Powered Data Science
FEBRUARY 15, 2023
Those F’s are: Fragility, Friction, and FUD (Fear, Uncertainty, Doubt). These rules are not necessarily “Rocket Science” (despite the name of this blog site), but they are common business sense for most business-disruptive technology implementations in enterprises.
The Unofficial Google Data Science Blog
APRIL 23, 2024
the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., This blog post discusses such a comprehensive approach that is used at Youtube. Crucially, it takes into account the uncertainty inherent in our experiments.
Cloudera
DECEMBER 3, 2021
In part 1 of this blog post, we discussed the need to be mindful of data bias and the resulting consequences when certain parameters are skewed. This involves identifying, quantifying and being able to measure ethical considerations while balancing these with performance objectives. We need to get to the root of the problem.
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