MLOps.community Lessons Learned From Hosting the Machine Learning Engineered Podcast // Charlie You // MLOps Coffee Sessions #28
Coffee Sessions #28 with Charlie You of Workday, Lessons learned from hosting the Machine Learning Engineered podcast.
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//Bio
Charlie You is a Machine Learning Engineer at Workday and the host of ML Engineered, a long-form interview podcast aiming to help listeners bring AI out of the lab and into products that people love. He holds a B.S. in Computer Science from Rensselaer Polytechnic Institute and previously worked for AWS AI.
Charlie is currently working as a Machine Learning Engineer at Workday. He hosts the ML Engineered podcast, learning from the best practitioners in the world.
Check Charlie's podcast and website here:
mlengineered.com
https://cyou.ai/
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Connect with Charlie on LinkedIn: https://linkedin.com/in/charlieyou/
Timestamps:
[00:00] Introduction to Charlie You
[01:50] Charlie's background in Machine Learning and inspiration to create a podcast
[06:20] What's your experience been so far as a machine learning engineer and trying to put models into production, and trying to get things out that have business value?
[07:08] "I started the podcast because as I started working, I had the tingling that machine learning engineering is harder than most people thought, and like way harder than I personally thought."
[08:20] What's an example of that where you target someone in your podcast, you keep that learning, and you want an extra meeting the next day and say, "Hey, actually I'm starting one of the world's experts on this topic and this is what they said"?
[10:06] In a world of tons of traditional software engineering assets and the process you put in place, how have they adopted what they're doing to the machine learning realm?
[19:00] About your podcast, what are some 2-3 most consistent trends that you've been seeing?
[21:08] Instead of splintering so much as a machine learning monitoring infrastructure specialist, are you going to departmentalize it in the future?
[27:22] Is there such a thing as an MLOps engineer right now?
[28:50] "We haven't seen a very vocal, very opinionated project manager in machine learning yet." - Todd Underwood
[30:18] "Similarly with tooling, we haven't seen the emergence of the tools that encode those best practices." Charlie
[31:42] "The day that you don't have to be a subject matter expert in machine learning to feel confident and deploy machine learning products, is the day that you will see the real product leadership in machine learning." Vishnu
[34:12] Security and Ethics
[34:41] "Data Privacy and Security is always at the top of any consideration for infrastructure." Charlie
[35:44] That's driven by legal requirements? How do you solve this problem?
[37:27] How do we make sure that if that blows up, you're not left with nothing?
[42:28] In your conversations, have you seen people who go with a cloud provider?
[43:25] Enterprises have much different incentives than startups do.
[45:48] What are some use cases where companies need to service their entire needs?
[45:48] What are some use cases where companies need to service their entire needs?
[49:18] What are some takeaways that you had in terms of how you think about your career, what experiences you want to build as this MLOps-based engineering is moving so fast?
[56:08] "Your edge is never in the algorithm"