Data Science Experimentation Uncertainty 
Rocket-Powered Data Science
FEBRUARY 15, 2023
Those F’s are: Fragility, Friction, and FUD (Fear, Uncertainty, Doubt). These changes may include requirements drift, data drift, model drift, or concept drift. encouraging and rewarding) a culture of experimentation across the organization. Fragility occurs when a built system is easily “broken” when some component is changed.
Domino Data Lab
MAY 15, 2019
This Domino Data Science Field Note covers Pete Skomoroch ’s recent Strata London talk. Pete indicates, in both his November 2018 and Strata London talks, that ML requires a more experimental approach than traditional software engineering. These steps also reflect the experimental nature of ML product management.
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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.
The Unofficial Google Data Science Blog
APRIL 23, 2024
Crucially, it takes into account the uncertainty inherent in our experiments. Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. It is a big picture approach, worthy of your consideration.
O'Reilly on Data
OCTOBER 13, 2020
Therefore, the PM should consider the team that will reconvene whenever it is necessary to build out or modify product features that: ensure that inputs are present and complete, establish that inputs are from a realistic (expected) distribution of the data, and trigger alarms, model retraining, or shutdowns (when necessary).
Cloudera
AUGUST 20, 2020
It’s been a year filled with disruption and uncertainty. Businesses had to literally switch operations, and enable better collaboration and access to data in an instant — while streamlining processes to accommodate a whole new way of doing things. One day we were all going to the office, and the next we were working from home.
The Unofficial Google Data Science Blog
JULY 22, 2020
Instead, we focus on the case where an experimenter has decided to run a full traffic ramp-up experiment and wants to use the data from all of the epochs in the analysis. When there are changing assignment weights and time-based confounders, this complication must be considered either in the analysis or the experimental design.
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