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Machine Learning in the Cloud, with TensorFlow

March 23, 2016

Posted by Slaven Bilac, Software Engineer, Google Research

At Google, researchers collaborate closely with product teams, applying the latest advances in Machine Learning to existing products and services - such as speech recognition in the Google app, search in Google Photos and the Smart Reply feature in Inbox by Gmail - in order to make them more useful. A growing number of Google products are using TensorFlow, our open source Machine Learning system, to tackle ML challenges and we would like to enable others do the same.

Today, at GCP NEXT 2016, we announced the alpha release of Cloud Machine Learning, a framework for building and training custom models to be used in intelligent applications.
Machine Learning projects can come in many sizes, and as we’ve seen with our open source offering TensorFlow, projects often need to scale up. Some small tasks are best handled with a local solution running on one’s desktop, while large scale applications require both the scale and dependability of a hosted solution. Google Cloud Machine Learning aims to support the full range and provide a seamless transition from local to cloud environment.

The Cloud Machine Learning offering allows users to run custom distributed learning algorithms based on TensorFlow. In addition to the deep learning capabilities that power Cloud Translate API, Cloud Vision API, and Cloud Speech API, we provide easy-to-adopt samples for common tasks like linear regression/classification with very fast convergence properties (based on the SDCA algorithm) and building a custom image classification model with few hundred training examples (based on the DeCAF algorithm).

We are excited to bring the best of Google Research to Google Cloud Platform. Learn more about this release and more from GCP Next 2016 on the Google Cloud Platform blog.