Amber Roberts

Amber Roberts

ML Growth Lead at Arize AI

    Bio Coming Soon!

    All Sessions by Amber Roberts

    Day 2 04/24/2024
    2:35 pm - 3:05 pm

    Tracing In LLM Applications

    <span class="etn-schedule-location"> <span class="firstfocus">LLMs</span> </span>

    According to a recent survey, 61.7% of enterprise engineering teams now have or are planning to have an LLM app in production within a year – and over one in ten (14.7%) are already in production, compared to 8.3% in April. With a record pace of adoption, the practice of troubleshooting and observing LLM apps takes on elevated importance. For software engineers that work with distributed systems, terms like “spans,” “traces,” and “calls” are well known. But what might these terms mean in a world where foundation models dominate? Since LLM observability isn’t just about tracking API calls, but about evaluating the LLM’s performance on specific tasks, there are a variety of span kinds and attributes that can be filtered on, in order to troubleshoot a LLM’s performance. Hosted by Amber Roberts – a data scientist, ML engineer and astrophysicist and former Carnegie Fellow – this session will focus on best practices for tracing calls in a given LLM application by providing the terminology, skills and knowledge needed to dissect various span kinds. Informed by work with dozens of enterprises with LLM apps in production and research on what works, attendees can learn span types and how to view traces from a LLM callback system and establish troubleshooting workflows to break down each call an application is making to an LLM. The session will explain and dive into both top-down workflows (starting with the big picture of the LLM use case and then getting into specifics of the execution if the performance is not satisfactory) and bottom-up workflows (discovery workflow where you are at the local level to filter on individual spans).

    Open Data Science

     

     

     

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