Extending AI: From Industry to Innovation

Overview

Dive into a comprehensive podcast episode featuring Sophia Rowland, Senior Product Manager, and David Weik, Senior Solutions Architect at SAS, as they explore the extension of AI from industry to innovation. Gain insights into optimizing the Data and AI lifecycle, implementing MLOps and LLMOPs across various industries, and leveraging analytical assets for better decision-making. Learn about best practices for deploying models in diverse scenarios, from IoT computer vision problems to composite flows with Large Language Models. Discover the challenges and solutions in next-generation MLOps, zombie model management, and the integration of generative AI. Explore governance issues in AI, evaluation challenges across industries, and the keys to success for high-performing AI teams. Benefit from expert discussions on usability challenges in interfaces, the frustrations with chatbots, and strategies for implementing successful AI agents.

Syllabus

[] Sophia & David's preferred coffee
[] Takeaways
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[] Hands on MLOps and AI
[] Next-Gen MLOps Challenges
[] Data scientists adopting software
[] Taking a different approach
[] Zombie Model Management
[] Optimizing ML Revenue Allocation
[] Other use cases - Lockout - Tagout procedure
[] Vision Model Integration Challenges
[] Costly errors in predictive maintenance
[] Integration of Gen AI
[] Governance challenges in AI
[] Governance in Gen AI vs Governance with Traditional ML
[] Evaluation challenges in industries
[] Interface frustration with Chatbots
[] Implementing AI Agent's success
[] Usability challenges in interfaces
[] Themes in High-Performing AI Teams
[] Wrap up