Machine Learning Engine
Managed Scalable Machine Learning
Google Cloud Machine Learning Engine 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. Cloud Machine Learning Engine can take any TensorFlow model and perform large scale training on a managed cluster.
Predictive Analytics 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 using state of the art optimization methods. It also lets you manage thousands of tuning experiments on the cloud—a process that would otherwise be extremely expensive. Cloud ML Engine enables 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 with GPU acceleration 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 Engine supports any TensorFlow models - you can build and use models that can work on any type of data, across a whole variety of scenarios.
CLOUD MACHINE LEARNING ENGINE FEATURES
Machine Learning on any data, any size
Google services are designed to work together. It works with Cloud Dataflow for feature processing, Cloud Storage for data storage andCloud Datalab for model creation.
Discover and Share Samples
Discover and share our Machine Learning Samples tailored to your industry use case.
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.
Focus on model development and prediction without worrying about the infrastructure. Managed service automates all resource provisioning and monitoring.
Build models of any data size or type using managed distributed training infrastructure that supports CPUs and GPUs. 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.
Use the open source TensorFlow SDK to train models locally on sample data sets and use the Google Cloud Platform for training at scale. Models trained using Cloud Machine Learning Engine can be downloaded for local execution or mobile integration.