CIO Business Intelligence

AUGUST 24, 2023

Gen AI has the potential to magnify existing risks around data privacy laws that govern how sensitive data is collected, used, shared, and stored. We’re getting bombarded with questions and inquiries from clients and potential clients about the risks of AI.” The risk is too high.”

Uncertainty 97

Uncertainty Risk Testing Enterprise 97

Rocket-Powered Data Science

FEBRUARY 15, 2023

3) How do we get started, when, who will be involved, and what are the targeted benefits, results, outcomes, and consequences (including risks)? Those F’s are: Fragility, Friction, and FUD (Fear, Uncertainty, Doubt). These changes may include requirements drift, data drift, model drift, or concept drift. Test early and often.

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

Domino Data Lab

FEBRUARY 20, 2019

Eugene Mandel , Head of Product at Superconductive Health , recently dropped by Domino HQ to candidly discuss cross-team collaboration within data science. Eugene Mandel , Head of Product at Superconductive Health , recently dropped by Domino HQ to discuss cross-team collaboration within data science.

Data Science 76

Data Science Testing Software Uncertainty 76

Domino Data Lab

MAY 15, 2019

This Domino Data Science Field Note covers Pete Skomoroch ’s recent Strata London talk. He also recommends that PMs refrain from “endless UI changes” on ML projects before the product is put before users because “seemingly small UI changes may result in significant back end ML engineering work” that may put the overall project at risk.

Machine Learning 111

Machine Learning Management Experimentation Uncertainty 111

IBM Big Data Hub

APRIL 18, 2024

Building an in-house team with AI, deep learning , machine learning (ML) and data science skills is a strategic move. Most importantly, no matter the strength of AI (weak or strong), data scientists, AI engineers, computer scientists and ML specialists are essential for developing and deploying these systems.

Cost-Benefit 93

Cost-Benefit Modeling Manufacturing Interactive 93

The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. We must therefore maintain statistical rigor in quantifying experimental uncertainty. In this post we explore how and why we can be “ data-rich but information-poor ”.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

by THOMAS OLAVSON Thomas leads a team at Google called "Operations Data Science" that helps Google scale its infrastructure capacity optimally. This classification is based on the purpose, horizon, update frequency and uncertainty of the forecast. A single model may also not shed light on the uncertainty range we actually face.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113