Remove Experimentation Remove Measurement Remove Testing
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Bringing an AI Product to Market

O'Reilly on Data

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. Without clarity in metrics, it’s impossible to do meaningful experimentation. When a measure becomes a target, it ceases to be a good measure ( Goodhart’s Law ).

Marketing 363
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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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Experimentation and Testing: A Primer

Occam's Razor

This post is a primer on the delightful world of testing and experimentation (A/B, Multivariate, and a new term from me: Experience Testing). Experimentation and testing help us figure out we are wrong, quickly and repeatedly and if you think about it that is a great thing for our customers, and for our employers.

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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.

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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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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

ML apps need to be developed through cycles of experimentation: due to the constant exposure to data, we don’t learn the behavior of ML apps through logical reasoning but through empirical observation. An Overarching Concern: Correctness and Testing. This approach is not novel. Why did something break? Who did what and when?

IT 350
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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

encouraging and rewarding) a culture of experimentation across the organization. Keep it agile, with short design, develop, test, release, and feedback cycles: keep it lean, and build on incremental changes. Test early and often. Encourage and reward a Culture of Experimentation that learns from failure, “ Test, or get fired!

Strategy 290