| 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. |
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.