I Am Once Again Asking "What is MLOps?" // Oleksandr Stasyk // #308

I Am Once Again Asking "What is MLOps?" // Oleksandr Stasyk // #308

Author: Demetrios April 22, 2025 Duration: 1:07:22

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 its 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 practices 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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Connect with Sash on LinkedIn: /oleksandr-stasyk-5751946b


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


Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
Author: Language: en-us Episodes: 100

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