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[00:03:19] The rise of new roles in data science
[00:04:10] What is it going to take, going forward, to start making money with machine learning and help companies on that road to maturity?
[00:06:54] What is an ML architect?
[00:09:13] Should a research oriented data scientist learn about architecture?
[00:12:41] Do you have to be a great software engineer to think like one?
[00:18:48] What is a feature store?
[00:20:57] The more I get into this data science/machine learning space…it's like the more I realized I don't know shit.
[00:23:22] Mikiko comes in with some awesome insight about feature sores
[00:28:48] When do I use a partition for a database?
[00:36:46] What are some other types of correlation?
[00:42:04] Thom with some wisdom.
[00:44:23] A question on web scraping (not people information, but product prices)
[00:55:17] The legality of web scraping
[00:58:15] How to understand how to help someone in the most effective way
[01:11:26] Figure out what the “ground truth” really is
[01:14:09] Why you need an emphasis on customer focus and how you can cultivate that mindset
Some useful links from our discussion
00:28:41 Joe Reis
00:37:55 Mikiko Bazeley
00:42:50 Mark Freeman
00:43:51 Harpreet Sahota
00:44:02 Mitul Patel
https://easystats.github.io/correlation/articles/types.html in R
00:54:25 Mikiko Bazeley
00:54:35 Mikiko Bazeley
00:55:43 Mark Freeman
00:56:54 Mark Freeman
01:06:32 Mark Freeman
01:24:45 Mark Freeman
01:27:27 Vikram Krishna Kotturu
01:27:45 Harpreet Sahota