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

rasmi 9y ago
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-scale-image.html 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.
TheIronYuppie 9y ago
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/ https://aws.amazon.com/blogs/aws/sagemaker/
- Bollack 9y ago
  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.
  - jorgemf 9y ago
    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.
    - joshuamorton 9y ago
      model shape is a hyperparameter ;)
      - jorgemf 9y ago
        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)
        joshuamorton 9y 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.

rasmi 9y ago

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-scale-image.html 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.

TheIronYuppie 9y ago

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/ https://aws.amazon.com/blogs/aws/sagemaker/

Bollack 9y ago
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.
- jorgemf 9y ago
  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.
  - joshuamorton 9y ago
    model shape is a hyperparameter ;)
    - jorgemf 9y ago
      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)
      - joshuamorton 9y 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.