// Abstract:

The fact is that most companies are barely doing BI, let alone AI. Joe discussed ways for companies to build a solid data foundation so they can succeed with machine learning. This meetup covers the continuum from cloud data warehousing to MLOps.

// Bio:

Joe is a Data Engineer and Architect, Recovering Data Scientist, 20 years in the data game.  Joe enjoys helping companies make sense of their culture, processes, and architecture so they can go from dreaming to doing. He’s certified in both AWS and Google Cloud. When not working, you can find Joe at one of the two groups he co-founded—The Utah Data Engineering Meetup and SLC Python. Joe also sits on the board of Utah Python, a non-profit dedicated to advocating Python in Utah.

https://www.youtube.com/channel/UC3H60XHMp6BrUzR5eUZDyZg

https://josephreis.com/

https://www.ternarydata.com/

https://www.linkedin.com/pulse/what-recovering-data-scientist-joe-reis/

https://www.linkedin.com/pulse/should-you-get-tech-certification-depends-joe-reis/

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

Connect with Joe on LinkedIn: https://www.linkedin.com/in/josephreis/  

Timestamps:

[00:23] How did you get into tech? What brought you on to the journey into data?

[04:50] You got into the auto ML and you decided to branch out and do your own thing? How did that happen?

[08:18] What is it with BI and then making that jump to ML?

[11:00] How have you seen Machine Learning fall flat with trying to shoehorn Machine Learning on top of the already weak foundation of BI?

[13:45] Let's imagine we're doing BI fairly well and now we want to jump to Machine Learning. Do we have to go out and reinvent the whole stack or can we shoehorn it on?

[15:36] How do you move from BI to ML?

[18:24] What do you mean by realtime?  

[20:35] Managed Services in DevOps

[23:30] The maturity isn't there yet

[26:03] Where would you draw the line between BI and AI?

[30:45] What are the things is Machine Learning an overkill for?

[33:43] Are you thinking about what data sets to collect and how different do those vary?

[35:18] "Software Engineering and Data Engineering are basically going to merge into one."

[38:27] What do you usually recommend moving from BI to AI?

[40:45] What is "strong data foundation" in your eyes?

[42:47] "MLFlow to gateway drug." What's your take on it?  

[46:25] In this pandemic, how easy is it for you to pivot to a new provider?

[49:10] Vision of companies starts coming together on different parts of the stack in the Machine Learning tools.