In the rapidly evolving field of artificial intelligence, the deployment of large language models (LLMs) presents both immense opportunities and complex challenges. To ensure responsible and effective innovation, it is crucial to have a comprehensive understanding of how to improve LLM applications to solve business use cases.
This presentation will provide insights into how monitoring can enhance the performance, reliability, and scalability of LLM systems. I will use monitoring and observability practices to demonstrate techniques for tracking model behavior, identifying bottlenecks, and diagnosing issues before they impact the end-user experience.
Join us to discover techniques and tools for integrating monitoring and observability into your LLM application. This will lead to significant improvements in system performance, user satisfaction, and overall application robustness. Whether you are an AI practitioner, developer, or system architect, this session will equip you with the knowledge to elevate your LLM applications to the next level.