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LLM Observability and Evaluations

Amber Roberts Amber Roberts | ML Engineer & Community Leader | Arize AI

The advent of LangChain and LlamaIndex has empowered rapid development of LLM-powered applications, yet introduced complexities in debugging due to their high-level abstractions. LLM Traces and Observability offer a solution by enabling insights into system operations without needing to understand internal mechanics, crucial for tackling unexpected issues. This requires applications to be well-instrumented, emitting necessary traces and logs for effective troubleshooting. LLM evaluation and tracing modules like Arize Phoenix exemplifies a key tool in ensuring observability, thereby facilitating smoother development and maintenance processes in LLM application development.

Amber Roberts
Amber Roberts
ML Engineer & Community Leader | Arize AI

Amber Roberts is a ML Growth Lead at Arize AI, a ML observability company built for maintaining models in production. Previously, Amber was a product manager of AI at Splunk and the Head of Artificial Intelligence at Insight Data Science. A Carnegie Fellow, Amber has an MS in Astrophysics from the Universidad de Chile.