The Importance of Data Engineering in Evolving ML Roles

I am once again asking "What is MLOps?" // MLOps Podcast #308 with Oleksandr Stasyk, Engineering Manager, ML Platform of Synthesia.

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// Abstract

What does it mean to MLOps now? Everyone is trying to make a killing from AI, everyone wants the freshest technology to show off as part of their product. But what impact does that have on the "journey of the model". Do we still think about how an idea makes it's way to production to make money? How can we get better at it, maybe the answer lies in the ancient "non-AI" past...

// Bio

For the majority of my career I have been a "full stack" developer with a leaning towards devops and platforms. In the last four years or so, I have worked on ML Platforms. I find that applying good software engineering practises is more important than ever in this AI fueled world.

// Related Links

Blogs: https://medium.com/@sashman90/mlops-the-evolution-of-the-t-shaped-engineer-a4d8a24a4042

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Timestamps:

[00:00] Sash's preferred coffee

[00:09] Takeaways

[01:21] Vibe Coding Reality Check

[06:27] MLOps and Vibe Coding

[12:53] Data Engineering in GenAI

[14:53] MLOps in MVP Development

[21:13] Platform Engineering Org Models

[27:30] Empathy in Data Engineering

[31:11] Post-DevOps MLOps Evolution

[39:32] AI for Fast Feedback

[46:53] AI Workflow vs Real Work

[50:13] ML Confession Stories

[59:06] Shift Left in Testing

[1:05:49] Wrap up