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owlninjaHNSource author
Lessons learnedExperience excerpt · English

You nailed where I currently stand

You nailed where I currently stand. At my company I've been a jack of all trades but mostly software/dba work. My boss and I were very excited about ML when the hype cycle was taking off several years ago and completed a successful project. Fast forward to today, I got loaned out to another team that lost their data scientist, and for the first time in my career I'm having to say - "I don't think we can do what you want." To me the "science" part really stands out. I have a decent grasp of methodologies and tools, but after weeks of dissecting the issue my conclusion is that they just don't have enough useful data...

Original discussionWhy is machine learning 'hard'? (2016)140 source comments · at import
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4death4HN

I used to work on an ML research team. In addition to what the author mentions, there is an entirely separate issue: whether or not what you're attempting to do is possible with the approach you've chosen. Consider making an iOS app. For the most part, an experienced software engineer can tell you if making a given app is possible, and they'll have a relatively clear idea about the steps required to realize the idea. Compare this to ML problems: you don't often know if your data or model selection can produce the results you want. On top of that, you don't know if you're not getting results because of a bug (i.e. the debugging issues mentioned by the author) or if because you have fundamental blocks elsewhere in the pipeline.

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jxramosHN

It can be a very empirical art. If you can't generate more data at the time you can sometimes invest in reviewing the hand labeling ground truth to verify no false classifications slipped by.

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j7akeHN

This is where good simulations are useful. If you can show even in ideal simple data scenarios, you encounter inference problems, it’s a strong signal that real data has little chance of doing better. In general these are model identifiability issues.

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PeterisPHN

Every data science team should have a wall decoration with John Tukey's quote "The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data."

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steve_adams_86HN

> they just don't have enough useful data... I'm not well-versed in this, or not as well as you are, but this has been my conclusion as well about a lot of ML project ideas from teams I've been on. You need so much data to do useful things. Especially the magical kinds of things people tend to want to do. I think these types of datasets are on a scale most software developers typically don't see. Even with the data in hand, it's nothing like trivial to determine how to do something half-way useful with it.

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