Join 📚 Kevin's Highlights

A batch of the best highlights from what Kevin's read, .

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From Day One to 100: The Seattle Data Guy Journey in One Special Issue

SeattleDataGuy

**Are you aware of the risks?** You need to know about the reasonably foreseeable risks and impact of your AI product before putting it on the market. If something goes wrong – maybe it fails or yields biased results – you can’t just blame a third-party developer of the technology. And you can’t say you’re not responsible because that technology is a “black box” you can’t understand or didn’t know how to test.

Keep Your AI Claims in Check

Federal Trade Commission

Here’s what the entity resolution query looks like: ![](https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F97ec307e-6b10-46fd-8b6a-773f9f163875_1212x728.png) I’m joining the table with itself on state + zipcode to reduce the search space and using string similarity thresholds for filtering potential duplicates. In entity resolution methodology this is known as “blocking.”

Fundamental Data Engineering Concepts - Part 2

Ergest Xheblati

...catch up on these, and many more highlights