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. Clean it, annotate it, catalog it, and bring it into the data family (connect the dots and see what happens). Test early and often. Test and refine the chatbot.

Strategy 289

Strategy Experimentation Uncertainty Machine Learning 289

DataKitchen

OCTOBER 2, 2023

.’ What’s a Data Journey? Data Journeys track and monitor all levels of the data stack, from data to tools to code to tests across all critical dimensions. A Data Journey supplies real-time statuses and alerts on start times, processing durations, test results, and infrastructure events, among other metrics.

Data Quality 100

Data Quality Testing Uncertainty Data Enablement 100

Cloudera

JULY 15, 2021

In this session we explored what firms are doing to approach the uncertainty with more predictability. Pandemic “Pressure” Testing. However, through this real-time “pressure test”, they identified areas of weakness, dependencies, and opportunities.

Risk 97

Risk Optimization Management Insurance 97

Depict Data Studio

MAY 12, 2020

Amanda went through some of the top considerations, from data quality, to data collection, to remembering the people behind the data, to color choices. COVID-19 Data Quality Issues. Are we including only cases that have been lab confirmed with a swab test that came back positive?

Visualization 52

Visualization Uncertainty Data Collection Data Quality 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

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