The Unofficial Google Data Science Blog

APRIL 23, 2024

Crucially, it takes into account the uncertainty inherent in our experiments. To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. In this section we’ll discuss how we approach these two kinds of uncertainty with QCQP.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Rocket-Powered Data Science

FEBRUARY 15, 2023

Those F’s are: Fragility, Friction, and FUD (Fear, Uncertainty, Doubt). encouraging and rewarding) a culture of experimentation across the organization. A business-disruptive ChatGPT implementation definitely fits into this category: focus first on the MVP or MLP. Test early and often. Test and refine the chatbot.

Strategy 289

Strategy Experimentation Uncertainty Machine Learning 289

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.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

The Unofficial Google Data Science Blog

JANUARY 14, 2016

Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Yet, this challenge is not insurmountable. for what is and isn’t possible) to address these challenges. Transcript.

Management 110

Management Machine Learning Experimentation Metrics 110

Occam's Razor

APRIL 8, 2013

Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. Testing out a new feature. Identify, hypothesize, test, react. But at the same time, they had to have a real test of an actual feature. You don’t need a beautiful beast to go out and test.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

The beliefs of this community are always evolving, and the process of thoughtfully generating, testing, refuting and accepting ideas looks a lot like Science. By definition, it is hard to disentangle causes and effects in these settings because it is rarely possible to isolate all relevant factors.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45