Cloud Machine Learning ^{Limited Preview}

Machine Learning on any data, of any size

Managed scalable machine learning platform

Google Cloud Machine Learning is a managed platform 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, which is powered by GPUs. Your trained model is immediately available for use with our global prediction platform that can support thousands of users and TBs of data. The platform 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

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 Platform

Scalable and distributed training infrastructure, powered by GPUs for your largest data sets. Managed no-ops infrastructure handles provisioning, scaling, and monitoring so that you can focus on building your models instead of handling clusters.

Deep Learning Capabilities

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.
Managed service: Focus on model development and prediction without worrying about the infrastructure. Managed service automates all resource provisioning and monitoring.
Scalable Platform: Build models of any data size or type using managed distributed training infrastructure. Accelerate model creation, by training across any number of nodes, that can optionally be powered by GPUs.

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.