Zilliz Webinar – Zoom

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About this Session

One of the most common use-cases for creating a LLM application is to connect an LLM to proprietary data such as enterprise documents. Vector Stores enable teams to connect their own data to LLMs. A common application is chatbots looking across a company’s knowledge base/context to answer specific questions. However, before these LLM apps get pushed to production, teams need to evaluate the performance of not just the retrieval, but also the responses.

Key Takeaways

• How LLM assisted evaluations work
• Statistical analysis of different retrieval strategies
• How to leverage an Evals library

Meet the Speaker

Join the session for live Q&A with the speaker

Jason Lopatecki is co-founder and CEO of Arize AI, a machine learning observability company. He is a garage-to-IPO executive with an extensive background in building marketing-leading products and businesses that heavily leverage analytics. Prior to Arize, Jason was co-founder and chief innovation officer at TubeMogul where he scaled the business into a public company and eventual acquisition by Adobe. Jason has hands-on knowledge of big data architectures, programmatic advertising systems, distributed systems, and machine learning and data processing architectures. In his free time, Jason tinkers with personal machine learning projects as a hobby, with a special interest in unsupervised learning and deep neural networks. He holds an electrical engineering and computer science degree from UC Berkeley – Go Bears!

Sally-Ann DeLucia is a ML Solutions Engineer at Arize AI, a leader in ML observability. A passionate machine learning enthusiast and deep learning specialist, she holds a master’s degree in Applied Data Science. Delucia combines a creative outlook with a dedication to developing solutions that are not only technically sound but also socially responsible.