Remove Experimentation Remove Reporting Remove Testing Remove Uncertainty
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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

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.

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Uncertainties: Statistical, Representational, Interventional

The Unofficial Google Data Science Blog

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.

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

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.

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Why CEOs should test big digital business ideas in tiny countries.

Mark Raskino

The President of Iceland Olafur Ragnar Grimsson explained this phenomenon to me when I had the privilege to interview him in 2011 (Gartner Report: G00212784 ). “So He was talking about something we call the ‘compound uncertainty’ that must be navigated when we want to test and introduce a real breakthrough digital business idea.

Testing 53
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CIOs press ahead for gen AI edge — despite misgivings

CIO Business Intelligence

If anything, 2023 has proved to be a year of reckoning for businesses, and IT leaders in particular, as they attempt to come to grips with the disruptive potential of this technology — just as debates over the best path forward for AI have accelerated and regulatory uncertainty has cast a longer shadow over its outlook in the wake of these events.

Risk 141
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AI Product Management After Deployment

O'Reilly on Data

In Bringing an AI Product to Market , we distinguished the debugging phase of product development from pre-deployment evaluation and testing. To support verification in these areas, a product manager must first ensure that the AI system is capable of reporting back to the product team about its performance and usefulness over time.

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20 issues shaping generative AI strategies today

CIO Business Intelligence

Just look at the stats:Some 45% of 2,500 executives polled for a May 2023 report from research firm Gartner said the publicity around ChatGPT prompted them to increase their AI investments, 70% said their organization is already exploring gen AI, and 19% are in actual pilot or production mode.