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Robust Experimentation and Testing | Reasons for Failure!

Occam's Razor

Since you're reading a blog on advanced analytics, I'm going to assume that you have been exposed to the magical and amazing awesomeness of experimentation and testing. And yet, chances are you really don’t know anyone directly who uses experimentation as a part of their regular business practice. Wah wah wah waaah.

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Taking measurements at parameter settings further from control parameter settings leads to a lower variance estimate of the slope of the line relating the metric to the parameter.

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Learn how to design, measure and implement trustworthy A/B tests from leading experimentation expert Ronny Kohavi (ex-Amazon, Airbnb, Microsoft)

KDnuggets

Leading expert Ronny Kohavi, drawing from his 20+ years of experience, will walk you through the ins and outs of experimentation, identifying key insights and working through live demos in his live course, Accelerating Innovation with A/B Testing, starting January 30th.

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IT pros: One-third of AI projects just for show

CIO Business Intelligence

AI projects without clear goals or measurable outcomes are unlikely to deliver real value. To ensure AI is aligned with strategic goals and poised to deliver measurable impact to customers and stakeholders, executives and boards need to prioritize education around AI,” she says. “By This trend is concerning,” he says. “AI

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When is the right time to dump an AI project?

CIO Business Intelligence

Still, a 30% failure rate represents a huge amount of time and money, given how widespread AI experimentation is today. One challenge with setting KPIs is measuring the results, adds Kathy Gersch, chief growth and commercial officer at Kotter International, a change management consulting firm.

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Bringing an AI Product to Market

O'Reilly on Data

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

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Practical Skills for The AI Product Manager

O'Reilly on Data

AI PMs should enter feature development and experimentation phases only after deciding what problem they want to solve as precisely as possible, and placing the problem into one of these categories. Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model.