5 ms·

This has to be very expensive for companies. A good business for Google

illumin8 9y ago
This seems to be a reaction by Google to the Amazon SageMaker release in November: https://aws.amazon.com/sagemaker/ https://aws.amazon.com/sagemaker/ It's great to see that other cloud providers are acknowledging the talent and training data gaps that many large enterprises face when adopting deep learning. Disclaimer: I work for AWS
- TheIronYuppie 9y ago
  Disclosure: I work at Google on Kubeflow This is an externalization of the service we use at Google internally called Vizier[1], first discussed publicly in June[2]. The idea is that instead of having to build a model yourself, we can use ML (yes, it uses ML to provide ML) to autotune your model and solve your business problem. Basically, instead of having to deal with all the steps in opening an editor, choosing a algo, tweaking, debugging, etc etc, just provide your structured or unstructured data and we'll help you answer your question (which is what customers actually care about). [1] https://research.google.com/pubs/pub46180.html https://research.google.com/pubs/pub46180.html [2] https://www.youtube.com/watch?v=Z2YL4XJKVpQ https://www.youtube.com/watch?v=Z2YL4XJKVpQ
  - illumin8 9y ago
    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.
  - TuringNYC 9y ago
    So an orthogonal approach here might be crowd-sourced centralized model zoos for better idea sharing across the entire industry. Curious how others see this (automated point solutions crafted to the data set) vs [hopefully soon popular] ONNX model zoos where we have more collaboration across orgs?
  - TheIronYuppie 9y ago
    /clarification AutoML vision is actually built on Google Brain’s proprietary image recognition technology, and Vizier is one of the components of their broader solution. You can see their earlier research announcement here[1]. Sorry to leave off the additional teams that helped in building this! [1] https://research.googleblog.com/2017/05/using-machine-learning-to-explore.html?m=1 https://research.googleblog.com/2017/05/using-machine-learni...
- jorgemf 9y ago
  How SageMaker is similar to AutoML? I don't see any reference in SageMaker about defining the architecture of your model for you based on your data.

This seems to be a reaction by Google to the Amazon SageMaker release in November: https://aws.amazon.com/sagemaker/ https://aws.amazon.com/sagemaker/ It's great to see that other cloud providers are acknowledging the talent and training data gaps that many large enterprises face when adopting deep learning. Disclaimer: I work for AWS

TheIronYuppie 9y ago
Disclosure: I work at Google on Kubeflow This is an externalization of the service we use at Google internally called Vizier[1], first discussed publicly in June[2]. The idea is that instead of having to build a model yourself, we can use ML (yes, it uses ML to provide ML) to autotune your model and solve your business problem. Basically, instead of having to deal with all the steps in opening an editor, choosing a algo, tweaking, debugging, etc etc, just provide your structured or unstructured data and we'll help you answer your question (which is what customers actually care about). [1] https://research.google.com/pubs/pub46180.html https://research.google.com/pubs/pub46180.html [2] https://www.youtube.com/watch?v=Z2YL4XJKVpQ https://www.youtube.com/watch?v=Z2YL4XJKVpQ
- illumin8 9y ago
  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.
- TuringNYC 9y ago
  So an orthogonal approach here might be crowd-sourced centralized model zoos for better idea sharing across the entire industry. Curious how others see this (automated point solutions crafted to the data set) vs [hopefully soon popular] ONNX model zoos where we have more collaboration across orgs?
- TheIronYuppie 9y ago
  /clarification AutoML vision is actually built on Google Brain’s proprietary image recognition technology, and Vizier is one of the components of their broader solution. You can see their earlier research announcement here[1]. Sorry to leave off the additional teams that helped in building this! [1] https://research.googleblog.com/2017/05/using-machine-learning-to-explore.html?m=1 https://research.googleblog.com/2017/05/using-machine-learni...
jorgemf 9y ago
How SageMaker is similar to AutoML? I don't see any reference in SageMaker about defining the architecture of your model for you based on your data.