The term "Responsible AI" has seen a threefold increase in search interest compared to 2020 across the globe. As developers, the questions like "How can we build large language model-enabled applications that are responsible and accountable to its users?" encountered in the conversation more often than before. And the discussion is further compounded by concerns surrounding uncertainty, bias, explainability, and other ethical considerations.

In this session, the speaker will guide you through fmeval, an open-source library designed to evaluate Large Language Models (LLMs) across a range of tasks. The library provides notebooks that you can integrate into your daily development process, enabling you to identify, measure, and mitigate potential responsible AI issues throughout your system development lifecycle.

Target Audience: Machine Learning Engineers/Data Scientists, AI/ML Researchers, Software Developers, AI/ML Project Managers, Solutions Architectures.