Aug 19 2017

Predictive Analytics – Cloud Machine Learning Engine #cloud #application #development #platform


Cloud 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. 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.

Predictive Analytics at Scale

Seamlessly transition from training to prediction, using online (currently in Beta) 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 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

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 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. 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. Models trained using Cloud Machine Learning Engine can be downloaded for local execution or mobile integration.

Google Cloud Machine Learning Engine enabled us to improve the accuracy and speed at which we correct visual anomalies in the images captured from our satellites. It solved a problem that has existed for decades. It will allow Airbus Defence and Space to continue to provide unrivaled access to the most comprehensive range of commercial Earth observation data available today

Mathias OrtnerData Analysis Image Processing Lead, Airbus Defense Space

Cloud Machine Learning Engine Pricing

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

Additional Resources

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