Data collection and labeling.
What's the hardest part of ML? The most expensive? The most time-consuming? Choosing from: - data collection & labelling - data cleaning - modelling / science - implementation - infrastructure / cloud SysOps - deployment - maintenance
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Data collection & labelling
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data collection & labeling for sure.
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GANs & unsupervised learning
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Data labeling as well as maintenance over time, so the first and the last. Labeling well is hard enough, but it's also common to create features that deteriorate over time due to missing a continuous process for updating labeled data and re-deploying.
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5 years ago, I would have said infra. Now I’d definitely say data collection & labelling.
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- forgetting to consider social implications until the product is already built?
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We have a winner!
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