Implementing LLM “judges” in production settings can be a game-changer for evaluating AI behaviors, but it’s often more challenging than it appears. Many teams struggle with common pitfalls such as; high error rates and cost unpredictability to issues with latency and long-term maintenance. This webinar will break down the top 10 misconceptions around LLM judges, equipping you with the insights to avoid these challenges and build more reliable, production-ready evaluation systems.
Join us to learn:
- Key Misconceptions: Understand where teams often go wrong in deploying LLM judges.
- EvalOps Principles: Discover best practices and tools to operationalize evaluations effectively.
- Reliability in Production: Learn how to make evaluation outcomes systematic, scalable, and dependable.
- Use Cases & Value: Explore real-world applications where LLM judges add significant value and the tangible results they deliver.
Who Should Attend:
This webinar is ideal for machine learning engineers, data scientists, AI practitioners, and technical leaders seeking to enhance their approach to model evaluation in production environments.
Due to limited funding in AI at the time, Ari started a SaaS company Dream Broker, with the goal of funding his future AI research. Dream Broker became Finland's fastest-growing company in 2012 according to Deloitte, serving over 1,000 enterprise and public sector clients.
In 2017, Ari returned to his AI passion, identifying generative models as the field's most promising frontier and completed his Ph.D. in Generative AI in 2021. With AGI becoming a mainstream concept in 2022, he founded Root Signals to advance this vision. Ari has published 10+ academic AI articles on venues including in NeurIPS, ICLR and AAAI.
