Adding Corporate Data to LLMs with Nicolas Decavel-Bueff
Speaking of Data
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Tracking and Managing Experiments in Machine Learning Projects
This chapter explores the significance of monitoring experiments in machine learning projects, emphasizing metrics for retrieval augmented generation and using large language models for evaluation. It also delves into ethical considerations like transparency, bias mitigation, and continuous testing, with a look at the challenges of eradicating bias and evolving trends in large language models.
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