Nik Suresh wrote an evisceration of the current AI hype boom called "I Will F**king Piledrive You If You Mention AI Again."
AI Operations Without Fundamental Engineering Discipline // MLOps Podcast #250 with Nikhil Suresh, Director @ Hermit Tech.
// Abstract
Nik is on the podcast because of an anti-AI hype piece, so a reasonable thing to discuss is going to be what most companies are getting wrong when non-technical management wants to immediately roll out ML initiatives, but are unwilling to bring technical naysayers on board who will set them up for success.
// Bio
Nik is the author of ludic.mataroa.blog, who wrote "I Will [REDACTED] Piledriver You If You Mention AI Again", and mostly works in the data engineering and data science spaces. Nik's writing and company both focus on bringing more care to work, pushing back against the industry's worst excesses both technically and spiritually, and getting fundamentals right.
Nik also has a reasonably strong background in psychology. His data science training was of the pre-LLM variety, circa. 2018, when there was a lot of hype but it wasn't this ridiculous.
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// Related Links
Website: https://ludic.mataroa.blog/
Nik's blog: https://ludic.mataroa.blog/blog/i-will-fucking-piledrive-you-if-you-mention-ai-again/
Harnessing MLOps in Finance // Michelle Marie Conway // MLOps Podcast Coffee #174: https://youtu.be/nIEld_Q6L-0
Fundamentals of Data Engineering: Plan and Build Robust Data Systems AudiobookBy: Joe Reis, Matt Housley: https://audiobookstore.com/audiobooks/fundamentals-of-data-engineering.aspx
Bullshit Jobs A Theory Hardcover
by David Graeber: https://www.amazon.co.jp/-/en/David-Graeber/dp/0241263883
Does a Frog have Scorpion Nature podcast: https://open.spotify.com/show/57i8sYVqxG4i3NvBniLfhv
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Timestamps:
[00:00] Nik's preferred coffee
[00:30] Takeaways
[01:40] Please like, share, leave a review, and subscribe to our MLOps channels!
[01:56] AI hype and humor
[07:21] Defining project success
[08:57] Effective data utilization
[12:18] AI Hype vs Data Engineering
[14:44] AI implementation challenges
[17:44 - 18:35] Data Engineering for AI and ML Virtual Conference Ad
[18:35] Managing AI Expectations
[22:08] AI expectations vs reality
[26:00] Balancing Engineering and AI
[31:54] Highlighting engineer success
[35:25] The real challenges
[36:30] Embracing work challenges
[37:21] Dealing with podcast disappointments
[40:50] Creating content for visibility
[43:02] Exploring niche interests
[44:14] Relationship building
[47:15] Strategic approach to success
[48:36] Wrap up