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Engineering

Machine Learning projects can come in many sizes, and as we’ve seen with GitHub’s 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 advanced computing solutions. Google Cloud Machine Learning is the only platform that supports the full range with Tensor Processing Units (TPUs) and provides a seamless transition from local to full secure cloud environment.

The Cloud Machine Learning offering allows users to run custom distributed learning algorithms based on TensorFlow, the #1 open source machine learning project on GitHub. In addition to the deep learning capabilities that power Cloud Translate API, Cloud Vision API, and Cloud Speech API, Google Cloud Platform provides 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 a few hundred training examples (based on the DeCAF algorithm).

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The Google advantage starts a project above others. With the most technologically advanced cloud computing platform hosting your project challenges common to most are easily overcome.