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Comment by illumin8 | Hacker News Reader

Same idea for Sagemaker. Nice to see I get a bunch of instant downvotes - I sometimes wonder why even bother participating in this community.

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I didn't downvote you, but I think the comparison to Sagemaker misses the point. This is literally just uploading labeled data and getting a finely tuned classifier out. Hyperparameter tuning is neat, and both Cloud ML Engine and Sagemaker have that, but (correct me if I'm wrong), only AutoML actually handles all of the model architecture decisions itself using transfer learning and learning2learn. See here for details: https://research.googleblog.com/2017/11/automl-for-large-sca...

This significantly reduces the level of expertise required to train models, and the AutoML models outperform "expert" human-created architectures.

Disclosure: I work at Google on Kubeflow

Interesting! I read up on Sagemaker here[1] and didn't see any AutoML style training/tuning features, but you would certainly know better than me :)

[1] https://aws.amazon.com/blogs/aws/sagemaker/

As far as I know, they haven't implemented the HPO on Sagemaker. They're planning to implement it soon. There's still no date announced.

HPO means Hyper Parameter Optimization? Because AutoML has nothing to do with it, AutoML is mostly about the architecture of the model, not about Hyper Parameter Optimization.

model shape is a hyperparameter ;)

for the same logic the researcher is another hyperparameter

(I know you are right, but so many people here think AutoML is exactly the same that the HPO they were doing since long time ago)

That's fair, yes automl is not simply tuning the learning rate and picking your favorite nonlinearity, its fancier than that, but its still tuning hyperparamters.

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