AWS Big Data

SEPTEMBER 5, 2024

Intuitively, for some extremely short user inputs, the vectors generated by dense vector models might have significant semantic uncertainty, where overlaying with a sparse vector model could be beneficial. Experimental data selection For retrieval evaluation, we used to use the datasets from BeIR.

Metrics 100

Metrics Testing Experimentation Modeling 100

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.

Experimentation 95

Experimentation Statistics Testing Knowledge Discovery 95

The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. We must therefore maintain statistical rigor in quantifying experimental uncertainty. In this post we explore how and why we can be “ data-rich but information-poor ”. And an LSOS is awash in data, right?

Experimentation 41

Experimentation Statistics Metrics Measurement 41

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

Despite a very large number of experimental units, the experiments conducted by LSOS cannot presume statistical significance of all effects they deem practically significant. The result is that experimenters can’t afford to be sloppy about quantifying uncertainty. At Google, we tend to refer to them as slices.

Experimentation 40

Experimentation Statistics Metrics Measurement 40

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 89

Cost-Benefit Manufacturing Modeling Interactive 89

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.

Management 110

Management Machine Learning Experimentation Metrics 110

Occam's Razor

APRIL 8, 2013

They might deal with uncertainty, but they're not random. Advanced Analytics Big Data Digital Analytics Web Analytics Web Insights Web Metrics actionable analytics business optimization experimentation and testing key performance indicators'

Metrics 157

Metrics KPI Analytics Key Performance Indicator 157