, can y'all communicate basics about how ML pipelines use signals to make rank predictions? E.g: model selection, data collection, training & retraining algos. This may help SEOs understand why testing for impact isn't always possible & why leaps of faith are necessary.
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Or SEOs could read Google patents about machine learning from Google search engineers, and learn about those subjects. They won't learn everything, but could learn some of the basics.
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There's also 's awesome Making Friends with Machine Learning series (starting youtube.com/watch?v=lYWt-a ), which used to be a Google-internal course. There are technical bits, but overall it's very understandable for non-engineers. It doesn't explain search rankings tho:)
This is a great suggestion. I have been watching videos from Cassie Kozyrkov for the past couple of months, and recommend that anyone doing SEO do the same:
Cassie Kozyrkov
AI is Decision making at Scale:
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