Features

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  Portable models

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    Use the open source TensorFlow SDK or other supported ML frameworks to train models locally on sample datasets, and use the Google Cloud Platform for training at scale. Models trained using Cloud ML Engine can be downloaded for local execution or mobile integration. You can also import scikit-learn, XGBoost, Keras, and TensorFlow models that have been trained anywhere for fully-managed, real-time prediction hosting — no Docker container required.

    
    
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  Integrated

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    Cloud ML Engine has deep integration with our managed notebook service and our data services for machine learning: Cloud Dataflow for feature processing, BigQuery for dashboard support and analysis, and Cloud Storage for data storage.

    
    
    
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  HyperTune

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    Achieve better results faster by automatically tuning deep learning hyperparameters with HyperTune. Data scientists often manage thousands of tuning experiments on the cloud. HyperTune saves many hours of tedious and error-prone work.

    
    
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  Automatic resource provisioning

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    Cloud ML Engine is a managed service which automates all resource provisioning and monitoring; builds models using managed distributed training infrastructure that supports CPUs, GPUs, and TPUs; and accelerates model development by training across many nodes or running multiple experiments in parallel. So you can focus on model development and deployment without worrying about infrastructure.