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. Keep it agile, with short design, develop, test, release, and feedback cycles: keep it lean, and build on incremental changes. Test early and often. Launch the chatbot.

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

Domino Data Lab

MAY 15, 2019

While the talk provides both organizational foundations for machine learning as well as product management insights to consider when shipping ML projects, I will be focusing on the latter in this blog post. These steps also reflect the experimental nature of ML product management. more probabilistic rather than deterministic).

Machine Learning 111

Machine Learning Management Experimentation Uncertainty 111

The Unofficial Google Data Science Blog

APRIL 23, 2024

This blog post discusses such a comprehensive approach that is used at Youtube. 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.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

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

IBM Big Data Hub

APRIL 18, 2024

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production. The AGI would need to handle uncertainty and make decisions with incomplete information.

Cost-Benefit 90

Cost-Benefit Modeling Manufacturing Interactive 90

The Unofficial Google Data Science Blog

JULY 22, 2020

Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure. We offer two examples where this may be the case.

Experimentation 95

Experimentation Statistics Testing Strategy 95