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.

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. Keep it agile, with short design, develop, test, release, and feedback cycles: keep it lean, and build on incremental changes. Test early and often.

Strategy 289

Strategy Experimentation Uncertainty Machine Learning 289

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

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. Operations.

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

O'Reilly on Data

MARCH 31, 2020

Machine learning adds uncertainty. The model outputs produced by the same code will vary with changes to things like the size of the training data (number of labeled examples), network training parameters, and training run time. Underneath this uncertainty lies further uncertainty in the development process itself.

Management 332

Management Machine Learning Experimentation Metrics 332

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. You should first identify potential compliance risks, with each additional step again tested against risks. Recognizing and admitting uncertainty is a major step in establishing trust.

Uncertainty 52

Uncertainty Machine Learning Advertising Risk 52

Domino Data Lab

JUNE 23, 2019

As a result, Skomoroch advocates getting “designers and data scientists, machine learning folks together and using real data and prototyping and testing” as quickly as possible. These measurement-obsessed companies have an advantage when it comes to AI. It is similar to R&D. Transcript. Hi, I’m Peter Skomoroch.

Management 110

Management Machine Learning Experimentation Metrics 110