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Reclaiming the stories that algorithms tell

O'Reilly on Data

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.

Risk 360
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Data Observability and Monitoring with DataOps

DataKitchen

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
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What Are the Most Important Steps to Protect Your Organization’s Data?

Smart Data Collective

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).

Testing 131
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Structural Evolutions in Data

O'Reilly on Data

” Consider the structural evolutions of that theme: Stage 1: Hadoop and Big Data By 2008, many companies found themselves at the intersection of “a steep increase in online activity” and “a sharp decline in costs for storage and computing.” Millions of tests, across as many parameters as will fit on the hardware.

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What is ArchiMate?

erwin

In 2008, the ownership of the brand was transferred to The Open Group, who have since revised and upgraded the standard. It’s tried and tested from an enterprise perspective and an in-demand certification for enterprise architects, so there are relatively low risks associated with adopting it. History of ArchiMate.

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Managing machine learning in the enterprise: Lessons from banking and health care

O'Reilly on Data

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.

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Real-Real-World Programming with ChatGPT

O'Reilly on Data

And if I switch tabs to view a paper from 2008, then a song from 2008 could start up. To provide some coherence to the music, I decided to use Taylor Swift songs since her discography covers the time span of most papers that I typically read: Her main albums were released in 2006, 2008, 2010, 2012, 2014, 2017, 2019, 2020, and 2022.