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Progress in the last decade shows that the success of an ML system depends largely on the data it was trained on. Instead of focusing on improving ML algorithms, most companies focus on managing and improving their data.
Garbage in and garbage out. If an ML engineer is not obsessed about data quality (e.g. feature/label correctness, dataset distributional properties, etc), it's a clear indication that they don't understand how ML actually works.
Dima’s 2024 Year in Books
Take a look at Dima’s Year in Books, including some fun facts about their reading.
152 books
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1,173 friends
44 books
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57 friends
87 books
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153 books
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29 friends
86 books
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256 friends
0 books
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129 friends
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5 friends
1,077 books
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84 friends
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