Mindset Over Metrics: How to Approach AI Engineering | Hamel Husain
Chain of Thought
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Navigating AI Metrics and Error Analysis
This chapter critiques the reliance on generic metrics in AI engineering, highlighting how vanity metrics can mislead teams about actual performance. It advocates for custom evaluations and qualitative analysis to enhance understanding and decision-making in AI systems. Additionally, the chapter explores the importance of evolving skill sets and data literacy among engineers and the need for effective data visualization tools.
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