ML lead vs PM on eval-methodology layer independence. who's actually right here? [D]

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A recent discussion on Reddit highlighted a disagreement between an ML lead and a PM regarding the evaluation methodology layer in AI projects. The PM had used a framework she learned from a specialized AI PM cohort, which emphasized a layered defense approach for non-engineering PMs. However, the ML lead argued that these layers were not independent but statistically conditioned, questioning the PM’s claim of independence.

This disagreement underscores the challenge of bridging different perspectives—between technical experts and those without the same level of engineering background—and how to ensure that evaluation methodologies remain robust and aligned with practical realities. For teams handling evaluations between ML/AI engineers and non-engineering PMs, this highlights the need for nuanced communication about statistical dependencies in layered approaches.

The simplified frameworks often used by non-technical stakeholders can lead to misunderstandings when applied to complex AI systems.
There is a need for more formalized training and documentation that explicitly addresses these nuances, ensuring all parties are on the same page.
Negotiations should focus not just on adopting best practices but also on understanding the statistical underpinnings of evaluation methodologies to prevent future conflicts.

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Originally published at reddit.com. Curated by AI Maestro.

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