Good question. We have been doing this for almost 2 years now and we still find new players almost every week! It's a bit of a wild west for sure.
I can't say what we do different from everyone, but a few things that we focus on:
* Speed: we train models based on DL in seconds. So you get real-time feedback on your model/data as you annotate and upload more. This is true for a few, but far from all of our competitors. In our benchmarking we find that we still perform on par with the competition (at least in the "low-data" regime https://www.nyckel.com/blog/automl-benchmark-nyckel-google-h...)
* Level of abstraction: Many competitors expose some ML knobs for their users thinking it will improve the experience. We found that this induces "ML anxiety" for many. As a result we have zero knobs. Just focus on your data, we do the rest.
* API: we have spend a ton of time developing clean API abstractions. Some competitors have great APIs, other don't.
* Cost: we are super cheap. Our lowest tier if $50. We don't charge for training or per function/model.
I can't say what we do different from everyone, but a few things that we focus on: * Speed: we train models based on DL in seconds. So you get real-time feedback on your model/data as you annotate and upload more. This is true for a few, but far from all of our competitors. In our benchmarking we find that we still perform on par with the competition (at least in the "low-data" regime https://www.nyckel.com/blog/automl-benchmark-nyckel-google-h...) * Level of abstraction: Many competitors expose some ML knobs for their users thinking it will improve the experience. We found that this induces "ML anxiety" for many. As a result we have zero knobs. Just focus on your data, we do the rest. * API: we have spend a ton of time developing clean API abstractions. Some competitors have great APIs, other don't. * Cost: we are super cheap. Our lowest tier if $50. We don't charge for training or per function/model.