Remove Consulting Remove Experimentation Remove Measurement
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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

ML apps needed to be developed through cycles of experimentation (as were no longer able to reason about how theyll behave based on software specs). The skillset and the background of people building the applications were realigned: People who were at home with data and experimentation got involved! How will you measure success?

Testing 168
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Measuring Incrementality: Controlled Experiments to the Rescue!

Occam's Razor

This: You understand all the environmental variables currently in play, you carefully choose more than one group of "like type" subjects, you expose them to a different mix of media, measure differences in outcomes, prove / disprove your hypothesis (DO FACEBOOK NOW!!!), Measuring Incrementality: Controlled Experiments to the Rescue!

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The DataOps Vendor Landscape, 2021

DataKitchen

DataOps requires that teams measure their analytic processes in order to see how they are improving over time. Comet.ML — Allows data science teams and individuals to automagically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility. Azure DevOps.

Testing 300
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Excellent Analytics Tip #8: Measure the Real Conversion Rate & "Opportunity Pie"

Occam's Razor

Mostly because short term goals drive a lot of what we do and if you are selling something on your website then it only seems to make logical sense that we measure conversion rate and get it up as high as we can as fast as we can. So measure Bounce Rate of your website. Even though we should not obsess about conversion rate we do.

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10 Fundamental Web Analytics Truths: Embrace 'Em & Win Big

Occam's Razor

Part of it fueled by some Consultants. Too many new things are happening too fast and those of us charged with measuring it have to change the wheels while the bicycle is moving at 30 miles per hour (and this bicycle will become a car before we know it – all while it keeps moving, ever faster). Part of it fueled by Vendors.

Analytics 119
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AI Product Management After Deployment

O'Reilly on Data

In an incident management blog post , Atlassian defines SLOs as: “the individual promises you’re making to that customer… SLOs are what set customer expectations and tell IT and DevOps teams what goals they need to hit and measure themselves against. While useful, these constructs are not beyond criticism.

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Rushing for AI ROI? Chances are it will cost you

CIO Business Intelligence

Measure everything Looking for ROI too soon is often a product of poor planning, says Rowan Curran, an AI and data science analyst at Forrester. Organizations rolling out AI tools first need to set reasonable expectations and establish key metrics to measure the value of the deployment , he says.

ROI 131