Getting value out of an ML with Philip Howes
The InfoQ Podcast
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Is Data a Bottleneck in Machine Learning?
Data is obviously very importantight? So then the question is, like, aa, how do we get data? Obviously, there are open data sets, and that usually gives us a great place to start. But you know, how many domain specific data sets are there? There's not that many. Data is relatively cheap to generate, but having high quality labors with its data. Yes, i think we now talked a bit about going from zero and having nothing to one where you create on value. How would you go at extending your data in a variable way?
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