The Unofficial Google Data Science Blog

JULY 18, 2023

E ven after we account for disagreement, human ratings may not measure exactly what we want to measure. How do we think about the quality of human ratings, and how do we quantify our understanding is the subject of this post. While human-labeled data is critical to many important applications, it also brings many challenges.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

DataKitchen

SEPTEMBER 21, 2023

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability In a world where 97% of data engineers report burnout and crisis mode seems to be the default setting for data teams, a Zen-like calm feels like an unattainable dream. What is Data in Use?

Testing 176

Testing Data Quality Predictive Modeling Metrics 176

Rocket-Powered Data Science

FEBRUARY 15, 2023

Those F’s are: Fragility, Friction, and FUD (Fear, Uncertainty, Doubt). These changes may include requirements drift, data drift, model drift, or concept drift. Fragility occurs when a built system is easily “broken” when some component is changed.

Strategy 289

Strategy Experimentation Uncertainty Machine Learning 289

Jen Stirrup

NOVEMBER 15, 2022

The foundation should be well structured and have essential data quality measures, monitoring and good data engineering practices. Systems thinking helps the organization frame the problems in a way that provides actionable insights by considering the overall design, not just the data on its own.

Data Science 145

Data Science Digital Transformation Data-driven Measurement 145

O'Reilly on Data

MARCH 31, 2020

Machine learning adds uncertainty. Underneath this uncertainty lies further uncertainty in the development process itself. There are strategies for dealing with all of this uncertainty–starting with the proverb from the early days of Agile: “ do the simplest thing that could possibly work.”

Management 332

Management Machine Learning Experimentation Metrics 332

DataRobot

JULY 16, 2021

They all serve to answer the question, “How well can my model make predictions based on data?” In performance, the trust dimensions are the following: Data quality — the performance of any machine learning model is intimately tied to the data it was trained on and validated against.

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

DataRobot

JUNE 1, 2021

In an earlier post, I shared the four foundations of trusted performance in AI : data quality, accuracy, robustness and stability, and speed. Recognizing and admitting uncertainty is a major step in establishing trust. Interventions to manage uncertainty in predictions vary widely. Knowing When to Trust a Model.

Uncertainty 52

Uncertainty Machine Learning Advertising Risk 52