Remove Data Collection Remove Measurement Remove Testing Remove Uncertainty
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What you need to know about product management for AI

O'Reilly on Data

Machine learning adds uncertainty. The model outputs produced by the same code will vary with changes to things like the size of the training data (number of labeled examples), network training parameters, and training run time. Underneath this uncertainty lies further uncertainty in the development process itself.

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

Occam's Razor

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. Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. Testing out a new feature.

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Measuring Validity and Reliability of Human Ratings

The Unofficial Google Data Science Blog

E ven after we account for disagreement, human ratings may not measure exactly what we want to measure. Overview Human-labeled data is ubiquitous in business and science, and platforms for obtaining data from people have become increasingly common. And for thousands of years, measurement was as simple as this.

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Leading infrastructure to accelerate electric power intelligence

CIO Business Intelligence

However, new energy is restricted by weather and climate, which means extreme weather conditions and unpredictable external environments bring an element of uncertainty to new energy sources. communication reliability, which supports minute-level data collection and second-level control for low-voltage transparency.

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Viral, Social, Sentiment, Mobile: 4 Delightful Web Analytics Solutions

Occam's Razor

Let's go look at some tools… Measuring "Invisible Virality": Tynt. It measures how often a blog post is tweeted/retweeted. I also measure the # of Comments Per Post as a measure of how "engaging" / "valuable" people found the content to be. Or for that matter how many tools.

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Product Management for AI

Domino Data Lab

As a result, Skomoroch advocates getting “designers and data scientists, machine learning folks together and using real data and prototyping and testing” as quickly as possible. These measurement-obsessed companies have an advantage when it comes to AI. It is similar to R&D. Transcript. Hi, I’m Peter Skomoroch.

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Misleading Statistics Examples – Discover The Potential For Misuse of Statistics & Data In The Digital Age

datapine

With the rise of advanced technology and globalized operations, statistical analyses grant businesses an insight into solving the extreme uncertainties of the market. Exclusive Bonus Content: Download Our Free Data Integrity Checklist. Get our free checklist on ensuring data collection and analysis integrity!