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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., This blog post discusses such a comprehensive approach that is used at Youtube. Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate.

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MNIST Expanded: 50,000 New Samples Added

Domino Data Lab

This has led to researchers to look for ways to address the rising danger of overfitting by reconstructing datasets, measuring the accuracy, and then sharing their process. 2018 , 2019 ], albeit on a different dataset and in a substantially more controlled setup. — Yann LeCun (@ylecun) May 29, 2019. Resources to Consider.

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Some highlights from 2020

Data Science and Beyond

The Australian bushfires of 2019-20 provided me with extra motivation to help nudge Automattic to do more in the fight against climate change. The initial covid-19 lockdown provided me with extra free time to make the measurement and offsetting of Automattic’s emissions from data centre power use happen. Technical work.

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How to create a culture of innovation

CIO Business Intelligence

Prioritize time for experimentation. It requires bold bets and a willingness to persevere despite setbacks, criticism, and uncertainty,’’ wrote McKinsey senior partners Laura Furstenthal and Erik Roth in a recent blog post. “By Here, they and others share seven ways to create and nurture a culture of innovation.

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The DataOps Vendor Landscape, 2021

DataKitchen

Read the complete blog below for a more detailed description of the vendors and their capabilities. DataOps requires that teams measure their analytic processes in order to see how they are improving over time. Acquired by DataRobot June 2019). Download the 2021 DataOps Vendor Landscape here. DataOps is a hot topic in 2021.

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Themes and Conferences per Pacoid, Episode 6

Domino Data Lab

Spoiler alert: a research field called curiosity-driven learning is emerging at the nexis of experimental cognitive psychology and industry use cases for machine learning, particularly in gaming AI. The ability to measure results (risk-reducing evidence). Ensure a culture that supports a steady process of learning and experimentation.

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Themes and Conferences per Pacoid, Episode 9

Domino Data Lab

Finale Doshi-Velez, Been Kim (2017-02-28) ; see also the Domino blog article about TCAV. Visualizations are vital in data science work, with the caveat that the information that they convey may be 4-5 layers of abstraction away from the actual business process being measured. measure the subjects’ ability to trust the models’ results.