Dataiku
OCTOBER 16, 2020
For designing machine learning (ML) models as well as for monitoring them in production, uncertainty estimation on predictions is a critical asset. It helps identify suspicious samples during model training in addition to detecting out-of-distribution samples at inference time.
CIO Business Intelligence
OCTOBER 4, 2022
From delightful consumer experiences to attacking fuel costs and carbon emissions in the global supply chain, real-time data and machine learning (ML) work together to power apps that change industries. more machine learning use casesacross the company. By Bryan Kirschner, Vice President, Strategy at DataStax.
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The Unofficial Google Data Science Blog
DECEMBER 14, 2021
by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature.
Domino Data Lab
MAY 15, 2019
Machine Learning Projects are Hard: Shifting from a Deterministic Process to a Probabilistic One. Over the years, I have listened to data scientists and machine learning (ML) researchers relay various pain points and challenges that impede their work. Product Management for Machine Learning.
Jedox
APRIL 16, 2020
Dean Boyer as a guest to the Jedox Blog for our series on “Managing Uncertainty” Mr. Boyer is a Director of Technology Services at Marks Paneth LLP, a premier accounting firm based in the United States. He shares his expertise on how an EPM solution supports managing economic uncertainty, particularly in times of crisis.
Jedox
APRIL 16, 2020
Dean Boyer as a guest to the Jedox Blog for our series on “Managing Uncertainty” Mr. Boyer is a Director of Technology Services at Marks Paneth LLP, a premier accounting firm based in the United States. He shares his expertise on how an EPM solution supports managing economic uncertainty, particularly in times of crisis.
Rocket-Powered Data Science
FEBRUARY 15, 2023
Those F’s are: Fragility, Friction, and FUD (Fear, Uncertainty, Doubt). Most of these rules focus on the data, since data is ultimately the fuel, the input, the objective evidence, and the source of informative signals that are fed into all data science, analytics, machine learning, and AI models.
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