DataKitchen

MAY 10, 2021

Some will argue that observability is nothing more than testing and monitoring applications using tests, metrics, logs, and other artifacts. Since 2008, teams working for our founding team and our customers have delivered 100s of millions of data sets, dashboards, and models with almost no errors. Tie tests to alerts.

Testing 214

Testing Manufacturing Data Quality Statistics 214

Smarten

JUNE 17, 2022

It was initiated in 2004 by the Department of Information Technology for implementing the provisions of the 2008 Information Technology Amendment Act. After completion of the testing procedure, the certificate is provided to show that all requirements were met.

Analytics 67

Analytics IT Business Intelligence Predictive Modeling 67

The Unofficial Google Data Science Blog

SEPTEMBER 30, 2016

The statistics app for Google Sheets hopes to change that. Editor's note: We've mostly portrayed data science as statistical methods and analysis approaches based on big data. Introduction Statistics for Google Sheets is an add-on for Google Sheets that brings elementary statistical analysis tools to spreadsheet users.

Statistics 53

Statistics Big Data Data Science Marketing 53

DataRobot Blog

APRIL 29, 2022

Prior to the financial crisis of 2008, Model Risk Management within the financial services industry was driven by industry best practices rather than regulatory standards(which brings to mind the saying “a fox guarding the hen house”). The regulators have provided a universal definition that has been adopted across the financial industry.

Risk 111

Risk Modeling IT Risk Management 111

O'Reilly on Data

MAY 27, 2020

Under school district policy, each of Audrey’s eleven- and twelve-year old students is tested at least three times a year to determine his or her Lexile, a number between 200 and 1,700 that reflects how well the student can read. These scores go on student report cards, and are a frequent topic at parent-teacher conferences.

Risk 355

Risk Testing Reporting Measurement 355

O'Reilly on Data

JULY 15, 2019

In recent posts, we described requisite foundational technologies needed to sustain machine learning practices within organizations, and specialized tools for model development, model governance, and model operations/testing/monitoring. AI projects in financial services and health care. Image by Ben Lorica. Sources of model risk.

Machine Learning 38

Machine Learning Management Enterprise Risk Management 38

Domino Data Lab

AUGUST 8, 2019

2008 – Financial crisis : scientists flee Wall St. Another key point: troubleshooting edge cases for models in production—which is often where ethics and data meet, as far as regulators are concerned—requires much more sophistication in statistics than most data science teams tend to have. It’s a quick way to clear the room.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71