Large language models (LLMs) can perpetuate societal biases, leading to discriminatory outputs. This lightning talk explores techniques for detecting and mitigating biases throughout the LLMOps lifecycle, from data collection to model deployment and monitoring. Learn best practices for building inclusive datasets, implementing debiasing strategies, conducting bias testing, and navigating ethical considerations. Through case studies and hands-on examples, participants will gain practical insights into tools and methodologies for embedding inclusion, diversity, and equity into LLMOps processes. This talk equips attendees with strategies to build more trustworthy LLMs, ensuring responsible development and deployment of these powerful technologies.
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