Oct. 28, 2022, 12:12 a.m. | /u/arkmastermind

Machine Learning www.reddit.com

I’ve been working as an applied ML engineer for almost a decade now, and in recent years I find I’m spending a lot less time fiddling with model architectures and a lot more time digging into the data - rebalancing the data, removing bad labels, figuring out features we should add, and sourcing new labeled examples of cases where the model is weak.

Often I’ve found this means collaborating with less-technical folks (e.g. PMs, managers, annotators, data domain experts, etc.) …

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