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. In isolation, the $x_1$-system is optimal: changing $x_1$ and leaving the $x_2$ at 0 will decrease system performance.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

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. Just-in-Time” manufacturing increases production while optimizing resources. Issue detected?

Manufacturing 87

Manufacturing Testing Experimentation Data Quality 87

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. Identification We now discuss formally the statistical problem of causal inference. We start by describing the problem using standard statistical notation.

Statistics 44

Statistics Optimization Modeling Experimentation 44

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. You can home in on an optimal value by specifying, say, 32 dimensions and varying this value by powers of 2. s lead may not be the optimal choice. Example 11.6 Pennington, J.,

Deep Learning 111

Deep Learning Modeling Metrics Testing 111