Hands-On Guide to Model Search: A Tensorflow-based Framework for AutoML

Hands-On Guide to Model Search: A Tensorflow-based Framework for AutoML

In this article, we have discussed Model Search, a flexible and domain agnostic TensorFlow framework for automated ML

When it comes to Deep Neural Network (DNNs), we are often confused about their architecture(like types of layers, number of layers, type of optimization, etc.) for a specific problem. This sudden template shift of using deep learning models for a various number of problems has made it even harder for researchers to design a new neural network and generalize it. In recent years, automated ML or AutoML has really helped researchers and developers to create high quality deep learning models without human intervention and to extend its usability, Google has developed a new framework called Model Search.

Model Search is an open-source, TensorFlow based python framework for building AutoML algorithms at a large scale. This framework allows :

  • To run many AutoML algorithms from the search of the right model architecture to the best-distilled models.
  • To compare different algorithms from search space.
  • To customize the neural network layers in search space.

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