Review: Google Cloud AutoML is truly automated machine learning

Google’s AutoML lets you create customized deep learning models without any knowledge of data science or programming

At a Glance

When you’re trying to train the best machine learning model for your data automatically, there’s AutoML, or automated machine learning, and then there’s Google Cloud AutoML. Google Cloud AutoML is a cut above. 

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In the past I’ve reviewed H2O Driverless AI, Amazon SageMaker, and Azure Machine Learning AutoML. Driverless AI automatically performs feature engineering and hyperparameter tuning, and claims to perform as well as Kaggle masters. Amazon SageMaker supports hyperparameter optimization. Azure Machine Learning AutoML automatically sweeps through features, algorithms, and hyperparameters for basic machine learning algorithms; a separate Azure Machine Learning hyperparameter tuning facility allows you to sweep specific hyperparameters for an existing experiment.

These are good, but Google Cloud AutoML goes to a whole different level and customizes Google’s battle-tested, high-accuracy deep neural networks for your tagged data. Rather than starting from scratch when training models from your data, Google Cloud AutoML implements automatic deep transfer learning (meaning that it starts from an existing deep neural network trained on other data) and neural architecture search (meaning that it finds the right combination of extra network layers) for language pair translation, natural language classification, and image classification.

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