Remove Experimentation Remove Modeling Remove Testing
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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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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

Let’s start by considering the job of a non-ML software engineer: writing traditional software deals with well-defined, narrowly-scoped inputs, which the engineer can exhaustively and cleanly model in the code. Not only is data larger, but models—deep learning models in particular—are much larger than before.

IT 364
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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

Weve seen this across dozens of companies, and the teams that break out of this trap all adopt some version of Evaluation-Driven Development (EDD), where testing, monitoring, and evaluation drive every decision from the start. What breaks your app in production isnt always what you tested for in dev! The way out?

Testing 174
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Best Practices for Creating Long-Lasting and Continuous Discovery Habits

Speaker: Teresa Torres, Internationally Acclaimed Author, Speaker, and Coach at ProductTalk.org

Industry-wide, product teams have adopted discovery practices like customer interviews and experimentation merely for end-user satisfaction. As a result, many of us are still stuck in a project-world rut: research, usability testing, engineering, and a/b testing, ad nauseam.

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End to End Statistics for Data Science

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Introduction to Statistics Statistics is a type of mathematical analysis that employs quantified models and representations to analyse a set of experimental data or real-world studies. Data processing is […].

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AI-native software engineering may be closer than developers think

CIO Business Intelligence

Despite critics, most, if not all, vendors offering coding assistants are now moving toward autonomous agents, although full AI coding independence is still experimental, Walsh says. With existing, human-written tests you just loop through generated code, feeding the errors back in, until you get to a success state.”

Software 141