Google Cloud Machine Learning hits public beta, with additions

Finally released from private alpha, Google Cloud Machine Learning lets all comers build software powered by trained algorithms

Google Cloud Machine Learning hits public beta, with additions

Google unveiled today machine learning-related additions to its cloud platform, both to enrich its cloud-based offerings and to offer expanded toolsets for businesses to develop their own machine learning-powered products.

The most prominent release was the public beta of Google Cloud Machine Learning, a platform for building and training machine learning models with the TensorFlow framework and data stored in the BigQuery and Cloud Storage back ends.

Google says its system simplifies the process of creating and deploying machine learning back ends for apps, in part simply by making models faster to train. Google claims Cloud Machine Learning's distributed training "can train models on terabytes of data within hours, instead of waiting for days."

However, a majority involves Cloud Machine Learning's APIs reducing the amount of programming required to build useful items. In a live demo, Google built and demonstrated a five-layer neural net for stock market analysis with a few mere lines of code.

Another announced feature, HyperTune, removes a source of drudgery often associated with building machine learning models. Models often need to have parameters tweaked to yield the best results. Google claims HyperTune "automatically improves predictive accuracy" by automating that step.

Google Cloud Machine Learning was previously only available as an alpha-level tech preview, but InfoWorld's Martin Heller was impressed with its pretrained APIs for artificial vision, speech, natural language, and language translation.

Many of the machine learning tools Google now offers, such as TensorFlow, arose from internal work to bolster its projects. The revamped version of Google's office applications, G Suite, is one of the latest to be dressed up with machine-learning-powered features. Most of the additions are for automating common busywork, such as finding a free time slot on a calendar to hold a meeting.

Google is now pitted against several other big-league cloud vendors offering their variations on the same themes, from IBM's Bluemix and Watson services to Microsoft's Azure Machine Learning. All of them, along with Amazon, Facebook, and others, recently announced the Partnership on AI effort to "study and formulate best practices on AI technologies" -- although it seems more like a general clearinghouse for public awareness about machine learning than an avenue for rivals to collaborate on shared projects.

Copyright © 2016 IDG Communications, Inc.