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.
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.
In each area, Google already has one or more pre-trained services based on deep neural networks and huge sets of labeled data. These may well work for your data unmodified, and you should test that to save yourself time and money. If these services don’t do what you need, Google Cloud AutoML helps you to create a model that does, without requiring that you know how to perform transfer learning or even how to create neural networks.