Google Updates Distributed Computing To Its TensorFlow Machine Learning Models

Google announced an update to its open-source framework TensorFlow that will now run training process for creating machine learning models over hundreds of machines aligned. According to The Verge, Google opened up its TensorFlow last year for companies that wants to build their own artificial intelligence application using the same open-source library the search engine applies to power everything from photo analytics and automated email replies. Google tried extending its platform to other computer servers by publicly releasing a version that could only run a single machine. Now, Google has updated a new version of TensorFlow with a feature that will enable to run distributed computing across multiple machines at the same time. Engineering leader of TensorFlow Rajat Monga said the reason why TensorFlow's multi-server version was delayed for release because they found it difficult to adapt the open-source software to be usable outside of the highly customized data centers of Google.