Who's MLOps for Anyway? // MLOps Podcast #261 with Jonathan Rioux, Managing Principal, AI Consulting at EPAM Systems.

// Abstract

The year is 2024 and we are all staring into the cliff towards the abyss of disillusionment for Generative AI. Every organization, developer, and AI-adjacent individual is now talking about "making AI real" and "turning a ROI on AI initiatives". MLOps and LLMOps are taking the stage as the solution; equip your AI teams with the best tools money can buy, grab tokens by the fistful, and look at value raking in.

Sounds familiar and eerily similar to the previous ML hype cycles?

From solo devs to large organizations, how can we avoid the same pitfalls as last time and get out of the endless hamster wheel?

// Bio

Jonathan is a Managing Principal of AI Consulting for EPAM, where he advises client on how to get from idea to realized AI products with the minimum of fuss and friction. He's obsessed with the mental models of ML and how to organize harmonious AI practices. Jonathan published "Data Analysis with Python and PySpark" (Manning, 2022).

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// Related Links

Website: raiks.ca

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Timestamps:

[00:00] Jonathan's preferred coffee

[00:25] Takeaways

[01:44] MLOps as not being sexy

[03:49] Do not conflate MLOps with ROI

[06:21] ML Certification Business Idea

[11:02] AI Adoption Missteps

[15:40] Slack AI Privacy Risks

[18:17] Decentralized AI success

[22:00] Michelangelo Hub-Spoke Model

[27:45] Engineering tools for everyone

[33:38 - 35:20] SAS Ad

[35:21] POC to ROI transition

[42:08] Repurposing project learnings

[46:24] Balancing Innovation and ROI

[55:35] Using classification model

[1:00:24] Chatbot evolution comparison

[1:01:20] Balancing Automation and Trust

[1:06:30] Manual to AI transition

[1:09:57] Wrap up