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Shreya Shankar
sh_reya
My thoughts on baselines, a concept that is *extremely* relevant in industry ML but does not exactly translate from academic ML: 1/9 In academic ML projects, my classmates and I
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I have been feeling tired lately when thinking about the differences between MLOps and DevOps. There are so many “gotchas” to keep track of in production ML systems, but I
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Thrilled to see a NeurIPS 2020 paper that eeks out a bit more performance on benchmark image datasets by...removing mislabeled examples from the train set!"Identifying Mislabeled Data using the Area
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Unit testing for ML pipelines is challenging given changing data, features, models, etc. Changing I/O make it hard to have fixed unit tests. To hackily get around this, I liberally
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Going to try another way of explaining why I think ML product dev is broken, this time with a clear software analogy: (1/6)https://twitter.com/sh_reya/status/1326313829534330880 Problem: some ML API product offerings are
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I'm not sure if the machine learning engineer role is very well-defined. IMO, a good MLE does "full-stack" work -- owning ML end-to-end, from model development to integration in production
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Got my invite to the @OpenAI GPT-3 API from @gdb. I actually think it deserves more hype than it’s getting, but not necessarily for the magical reasons Twitter touts. Why?
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