Cloud Machine Learning ^BETA

Machine Learning on any data, of any size

Managed scalable machine learning

Google Cloud Machine Learning is a managed service that enables you to easily build machine learning models, that work on any type of data, of any size. Create your model with the powerful TensorFlow framework that powers many Google products, from Google Photos to Google Cloud Speech. Build models of any size with our managed scalable infrastructure. Your trained model is immediately available for use with our global prediction platform that can support thousands of users and TBs of data. The service is integrated with Google Cloud Dataflow for pre-processing, allowing you to access data from Google Cloud Storage, Google BigQuery, and others.

Prediction at Scale

Seamlessly transition from training to prediction, using online and batch prediction services. Integration to Google global load balancing enables you to automatically scale your machine learning application, and reach users world-wide.

Build Machine Learning Models Easily

HyperTune lets you automatically tune your model training to achieve better results faster. Enable developers to easily build models using Cloud Datalab. Data Scientists can understand their data, create TensorFlow model graphs, train their models and analyze model quality.

Fully Managed Service

Scalable and distributed training infrastructure for your largest data sets. Managed serverless infrastructure handles provisioning, scaling, and monitoring so that you can focus on building your models instead of handling clusters.

Deep Learning Capabilities

Cloud Machine Learning supports any TensorFlow models - you can build and use models that can work on any type of data, across a whole variety of scenarios.

Machine Learning Features

Machine Learning on any data, any size

Integrated: Google services are designed to work together. It works with Cloud Dataflow for feature processing, Cloud Storage for data storage and Cloud Datalab for model creation.
HyperTune: Build better performing models faster by automatically tuning your hyperparameters with HyperTune, instead of spending many hours to manually discover values that work for your model.
Managed Service: Focus on model development and prediction without worrying about the infrastructure. Managed service automates all resource provisioning and monitoring.

Scalable Service: Build models of any data size or type using managed distributed training infrastructure. Accelerate model development, by training across many number of nodes, or running multiple experiments in parallel.
Notebook Developer Experience: Create and analyze models using the familiar Jupyter notebook development experience, with integration to Cloud Datalab.
Portable Models: Use the open source TensorFlow SDK to train models locally on sample data sets and use the Google Cloud Platform for training at scale. In future phases, models trained using Cloud Machine Learning can be downloaded for local execution.

ML Pricing

Cloud Machine Learning charges for training ML models and running predictions with trained models. For detailed pricing information, please view the pricing guide.

Item	US	Europe/Asia
Training Clusters
Basic Tier	$0.49/hour	$0.54/hour
Standard Tier	$4.90/hour	$5.40/hour
Premium Tier	$36.75/hour	$40.50/hour
Custom Cluster Configuration	$0.49/hour per ML training unit	$0.54/hour per ML training unit
Prediction Requests
Up to 100M per Month	$0.10 / 1K +$0.40/Node Hour	$0.11 / 1K +$0.44/Node Hour
Requests over 100M per month	$0.05 / 1K +$0.40/Node Hour	$0.05 / 1K +$0.44/Node Hour

Beta: This is a Beta release of Cloud ML. This feature is not covered by any SLA or deprecation policy and may be subject to backward-incompatible changes.

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