Reliable LLM Products Fueled by Feedback - MLOps Podcast #251

Dive into a comprehensive podcast episode exploring the critical aspects of developing reliable LLM products through user feedback. Learn from Chinar Movsisyan, CEO of Feedback Intelligence, as she shares insights on automating evaluation processes, enhancing user experience, and optimizing AI products for real-world deployment. Discover innovative approaches to measuring and improving LLM performance, particularly in highly regulated industries. Explore topics ranging from object detection in drones and street surveillance to challenges in plastic surgery projects and the rising popularity of diffusion models. Gain valuable knowledge on effective data analysis strategies, identifying key metrics, and bridging the gap between subject matter experts and engineers in AI development. Uncover the shifting role of AI metrics and the importance of balancing engineering with evaluation in creating successful LLM-based products.

[] Chinar's preferred coffee
[] Takeaways
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[] Object Detection on Drones
[] Street Surveillance Detection Use Case
[] Optimizing Vision Models
[] Data Engineering for AI/ML Conference Ad
[] Plastic surgery project
[] Diffusion models getting popular
[] AI challenges in highly regulated industries
[] Product metrics evaluation insights
[] Chatbot effectiveness metrics
[] Interpreting user signals
[] Metadata tracking in LLM
[] Agentic workflow
[] Effective data analysis strategies
[] Identifying key metrics
[] AI metrics role shift
[] Tooling for non-engineers
[] Balancing engineering and evaluation
[] Bridging SME engineering gap
[] Expand expertise potential
[] What's with flamingos
[] Wrap up