Remove Measurement Remove Metrics Remove Testing Remove Uncertainty
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You Can’t Regulate What You Don’t Understand

O'Reilly on Data

If we want prosocial outcomes, we need to design and report on the metrics that explicitly aim for those outcomes and measure the extent to which they have been achieved. And they are stress testing and “ red teaming ” them to uncover vulnerabilities. That is a crucial first step, and we should take it immediately.

Metrics 284
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The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

To win in business you need to follow this process: Metrics > Hypothesis > Experiment > Act. We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. That metric is tied to a KPI.

Metrics 156
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In AI we trust? Why we Need to Talk About Ethics and Governance (part 2 of 2)

Cloudera

This involves identifying, quantifying and being able to measure ethical considerations while balancing these with performance objectives. Systems should be designed with bias, causality and uncertainty in mind. Uncertainty is a measure of our confidence in the predictions made by a system. System Design. Model Drift.

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What you need to know about product management for AI

O'Reilly on Data

Machine learning adds uncertainty. This has serious implications for software testing, versioning, deployment, and other core development processes. Underneath this uncertainty lies further uncertainty in the development process itself. Measurement, tracking, and logging is less of a priority in enterprise software.

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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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ITIL certification guide: Costs, requirements, levels, and paths

CIO Business Intelligence

This module validates your ability to measure, assess, and develop the Service Desk practice capability using the ITIL Maturity Model. You’ll be tested on a situation of your choosing, so the material will be personal to your experience.

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Variance and significance in large-scale online services

The Unofficial Google Data Science Blog

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.