Explore the challenges and opportunities in implementing MLOps and LLMOps in this podcast episode featuring Jonathan Rioux, Managing Principal of AI Consulting at EPAM Systems. Delve into the current state of Generative AI, the push for ROI on AI initiatives, and how to avoid pitfalls from previous ML hype cycles. Learn about mental models for ML, organizing harmonious AI practices, and transitioning from proof-of-concept to realized AI products. Discover insights on decentralized AI success, the Michelangelo hub-spoke model, balancing innovation with ROI, and the evolution of chatbots. Gain valuable perspectives on AI adoption, privacy risks, and the transition from manual processes to AI-driven solutions in various organizational contexts.
Who's MLOps for Anyway? - Navigating AI Initiatives and ROI
Overview
Syllabus
[] Jonathan's preferred coffee
[] Takeaways
[] MLOps as not being sexy
[] Do not conflate MLOps with ROI
[] ML Certification Business Idea
[] AI Adoption Missteps
[] Slack AI Privacy Risks
[] Decentralized AI success
[] Michelangelo Hub-Spoke Model
[] Engineering tools for everyone
[33:38 - ] SAS Ad
[] POC to ROI transition
[] Repurposing project learnings
[] Balancing Innovation and ROI
[] Using classification model
[] Chatbot evolution comparison
[] Balancing Automation and Trust
[] Manual to AI transition
[] Wrap up
Taught by
MLOps.community
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