Ready for Agents

Gal shares the key principles that makes AI-adoption actually work for startups.

For more distilled agents knowledge:readyforagents.comLLMs observability best practice - Ready4Agents Ep. 3 #llm #agent In this conversation with TraceLoop's CTO Gal, we discuss the significance of observability in LLM applications, focusing on the challenges and best practices for monitoring and evaluating these complex systems. Chapters00:00 Introduction to LLM Observability02:51 Understanding TraceLoops05:59 Complexity of LLM Applications08:48 Best Practices for LLM Observability12:11 Technical Deep Dive into TraceLoops14:58 Tracing and Telemetry in LLMs17:51 Evaluating LLM Quality and Performance20:52 Implementation Considerations for LLMs Observability24:00 Optimizing LLM Usage in Production26:59 Future of LLM Observability29:57 Gal's recommended Resources

In this episode I had the privilege to host Matt who's wrapping up his time at UofC where he researched AI and distributed systems.

See full details:

https://www.readyforagents.com/resources/timeline-for-agi

AGI could be here according to Matt.

Matt shares the insights gained during his PhD at University of Chicago.

Dean has spoken to over 100 MLOps professionals over the last 5 years as the CEO of the ML Platform DaGsHub - in this episode he shares the best practices for ML projects and we create a blueprint for how to get started.

For more: https://www.youtube.com/channel/UCRRVYwJ-qt_5FbFVRId7BEg