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?

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. Issue detected?

Manufacturing 98

Manufacturing Testing Data Quality Dashboards 98

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 45

Statistics Optimization Modeling Experimentation 45

Alation

SEPTEMBER 28, 2022

DataOps as a term was brought to media attention by Lenny Liebmannin 2014, then popularized by several other thought leaders. Technical environments and IDEs must be disposable so that experimental costs can be kept to a minimum. Over the past 5 years, there has been a steady increase in interest in DataOps. Source: Google Trends.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

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. We waved our finger in the air to select 64, so some experimentation and optimization are warranted at your end if you feel like it. Example 11.6 Pennington, J., GloVe: Global vectors.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111