2014 Experimentation Statistics Testing 
Octopai
MAY 2, 2022
Common elements of DataOps strategies include: Collaboration between data managers, developers and consumers A development environment conducive to experimentation Rapid deployment and iteration Automated testing Very low error rates. Issue detected? This means that more focused development can happen faster. .
The Unofficial Google Data Science Blog
APRIL 23, 2024
If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. And we can keep repeating this approach, relying on intuition and luck. Why experiment with several parameters concurrently?
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Alation
SEPTEMBER 28, 2022
DataOps as a term was brought to media attention by Lenny Liebmannin 2014, then popularized by several other thought leaders. Automated testing to ensure data quality. Technical environments and IDEs must be disposable so that experimental costs can be kept to a minimum. Source: Google Trends. How Does DataOps Provide Value?
The Unofficial Google Data Science Blog
JUNE 30, 2016
In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. A naïve way to solve this problem would be to compare the proportion of buyers between the exposed and unexposed groups, using a simple test for equality of means.
Domino Data Lab
AUGUST 22, 2019
Although it’s not perfect, [Note: These are statistical approximations, of course!] At the time—in 2014—the three were colleagues working. Note: A test set of 19,500 such analogies was developed by Tomas Mikolov and his colleagues in their 2013 word2vec paper. Relative to extrinsic evaluations, intrinsic tests are quick.
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