Smart Data Collective
APRIL 13, 2021
Based on figures from Statista , the volume of data breaches increased from 2005 to 2008, then dropped in 2009 and rose again in 2010 until it dropped again in 2011. In 2009 for example, data breaches dropped to 498 million (from 656 million in 2008) but the number of records exposed increased sharply to 222.5 million in 2008).
O'Reilly on Data
NOVEMBER 29, 2022
The financial collapse of 2008 led to tighter regulation of banks and financial institutions. That kind of granular approach would make it easier to develop statistical tests that could determine if the solution is harming any of the groups. Good outcomes do not happen on their own.
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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.
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.
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.
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. They test each student’s grasp of a particular sentence or paragraph—but not of a whole story.
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.
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