Who's MLOps for Anyway? // Jonathan Rioux // #261

Sep 17, 2024

In this engaging discussion, Jonathan Rioux, Managing Principal of AI Consulting at EPAM Systems, shares insights on the evolving landscape of MLOps. He highlights the critical balance between technical prowess and business alignment needed for successful AI products. Rioux also navigates the misconceptions surrounding MLOps, emphasizing its true role beyond just technical execution. The conversation touches on the importance of user-friendly tech solutions, measuring ROI in chatbot technology, and innovative approaches to tackling challenges in generative AI.

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Intro

00:00 • 2min

Navigating MLOps: Reality vs. Perception

01:35 • 26min

Bridging the Gap: Designing User-Friendly Tech Solutions

27:45 • 6min

Unlocking Business Insights with SAS Decision Builder

33:42 • 5min

Measuring ROI in Chatbot Technology

38:47 • 11min

Innovative Approaches to Chatbot Development and Content Curation

49:35 • 4min

Innovations in Language Models

53:41 • 4min

Navigating Customer Data in Automated Support

57:13 • 13min

Jonathan Rioux is a Managing Principal of AI Consulting for EPAM Systems, where he advises clients on how to get from idea to realized AI products with the minimum of fuss and friction. 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). // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: raiks.ca --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Jonathan on LinkedIn: https://www.linkedin.com/in/jonathanrx/ 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