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I'd guess they have a very high drop-out rate. Regardless, "machine learning" is a very broad field and honestly I have no idea what an "ML engineer" is doing
by usaar333 7y ago
I'd guess they have a very high drop-out rate.
Regardless, "machine learning" is a very broad field and honestly I have no idea what an "ML engineer" is doing if they are one. It can cover any of the following:
1. Cutting-edge academic research (do better on this test set)
2. Doing data analysis to identify prediction ability
3. Creatively thinking of useful features to evaluate.
4. Implementing data pipelines/logging to obtain the features needed for #3.
5. Production systems to evaluate/train ML systems. (multiple places in the stack).
Because the spectrum is so wide, if you are already an engineer, you can readily get into "ML" categories 3-5 and even 2. Andrew Ng's course is a valuable introduction and not that heavy of an investment -- I found that just with it (alongside my product and infra background), I could readily contribute to ML groups at my company.
- YeGoblynQueenne 7y ago>> 1. Cutting-edge academic research (do better on this test set) It's interesting you put it this way. I think most machine learning researchers who aspire to do "cutting-edge" research would prefer to be the first one to do well on a new dataset, rather than push the needle forward by 0.005 on an old dataset that everyone else has already had a ball with. Or at the very least, they'd prefer to do significantly better than everyone else on that old dataset. I bet you remember the names of the guys who pushed the accuracy on ImageNet up by ~11%, but not the names of the few thousand people who have since improved results by tiny little amounts.