O'Reilly on Data

APRIL 19, 2021

We’ll look at this later, but being able to reproduce experimental results is critical to any science, and it’s a well-known problem in AI. This is significant and definitely worth watching over the next few months. But is application deployment the right metric for maturity? Bottlenecks to AI adoption. Maturity by Continent.

Enterprise 356

Enterprise Modeling Risk Manufacturing 356

Occam's Razor

JUNE 6, 2007

Bonus: Interactive CD: Contains six podcasts, one video, two web analytics metrics definitions documents and five insightful powerpoint presentations. Experimentation & Testing (A/B, Multivariate, you name it). In 480 pages the book goes from from beginner's basics to a advanced analytics concepts. Clicks and outcomes.

Analytics 60

Analytics Metrics Marketing Dashboards 60

Corinium

APRIL 25, 2019

Yes, definitely! Life insurance needs accurate data on consumer health, age and other metrics of risk. For example auto insurance companies offering to capture real-time driving statistics from policy-holders’ cars to encourage and reward safe driving. What do you recommend to organisations to help them overcome these?

Insurance 150

Insurance Risk IoT Cost-Benefit 150

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. Another pattern that I’ve seen in good PMs is that they’re very metric-driven.

Management 110

Management Machine Learning Experimentation Metrics 110

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

In this post we explore why some standard statistical techniques to reduce variance are often ineffective in this “data-rich, information-poor” realm. Despite a very large number of experimental units, the experiments conducted by LSOS cannot presume statistical significance of all effects they deem practically significant.

Experimentation 40

Experimentation Metrics Statistics Measurement 40

Domino Data Lab

MAY 8, 2019

In contrast, some researchers who are exploring the definitions, possibilities, and limitations of model interpretation point toward more comprehensive views. There’s no one-size-fits-all definition of what’s required across all verticals. Let’s look at two issues, both more social than technical, that bubble to the top of the list.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

By definition, it is hard to disentangle causes and effects in these settings because it is rarely possible to isolate all relevant factors. It is important to make clear distinctions among each of these, and to advance the state of knowledge through concerted observation, modeling and experimentation.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

Occam's Razor

MARCH 23, 2010

Part of it is fueled by a vocal minority genuinely upset that 10 years on we are still not a statistically powered bunch doing complicated analysis that is shifting paradigms. Because every tool uses its own sweet metrics definitions, cookie rules, session start and end rules and so much more. Likely not. success measures.

Analytics 118

Analytics Consulting Metrics Experimentation 118