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A batch of the best highlights from what Kevin's read, .
"You asked the impossible of a machine,"
said Salo,
"and the machine complied."
The Sirens of Titan
Kurt Vonnegut
As shocking as that might initially seem, we should not to be totally surprised.
Seven years of AI has taught us that deep learning is unpredictable, and not always human like.
“Adversarial attacks” like these have shown remarkable weakness, time and again, repeatedly establishing that what deep learning systems do just isn’t the same as what people do:
[](https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8f1f1fed-7908-4cc0-a29b-d88005ebaa10_2000x794.png)
David Beats Go-Liath
Gary Marcus
When we unpack the common threads of how various people define data engineering, an obvious pattern emerges:
a **data engineer**
*gets data, stores it, and prepares it for consumption*
by **data scientists**, **analysts**, and others.
We define data engineering and data engineer as follows:
**Data engineering** is
the *development*, *implementation*, and *maintenance*
of **systems** and **processes** that take in raw data
and produce high-quality, consistent information
that supports downstream use cases,
such as analysis and machine learning.
**Data engineering** is
the intersection of
*security*,
*data management*,
*DataOps*,
*data architecture*,
*orchestration*, and
*software engineering*.
A **data engineer**
*manages the data engineering lifecycle*,
beginning with getting data from source systems and
ending with serving data for use cases,
such as analysis or machine learning.
Fundamentals of Data Engineering
Reis, Joe;Housley, Matt;
...catch up on these, and many more highlights
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