Late last month Google released an alpha version of their TensorFlow (TF) integrated cloud machine learning service as a response to a growing need to make their Tensor Flow library to run at scale on the Google Cloud Platform (GCP).
Google describes in detail several new feature sets around making TF usage scale by integrating several pieces of the GCP like Dataproc, a managed Hadoop and Spark service. The open-source initiative TF and its recent integration with GCP “allows users to run custom distributed learning algorithms” on managed cloud services made generally available as part of the announcement, tightly coupling their big-data and analytics platforms with the machine learning space.
