Google’s Cloud Machine Learning service launched earlier this year and, already, the company is calling it one of its “fastest growing product areas.” Today, the company is announcing a number of new features for Cloud Machine Learning users and developers who want to run their own machine learning workloads in Google’s cloud.
Unlike its competitors, like AWS and Azure, Google never offered developers access to virtual machines with high-end graphics processing units (GPUs). Machine learning (as well as a number of other specialized workloads, mostly in the sciences) heavily depends on GPUs to power the core algorithms that have made this technique so successful.
Sadly, you’ll have to wait a bit before you can get started with running your own machine-learning workloads on the Google Cloud Platform. These new GPU-centric machines won’t launch until early 2017. Until then, Google won’t release pricing either.
It’s a bit of a puzzle to me why Google didn’t previously offer this kind of machine, especially given its own focus on machine learning and the fact that its competitors like Azure (which signed a partnership with OpenAI earlier today) and AWS already offered it.
Even then, you will still be able to use Google’s existing Cloud Machine Learning service (in combination with TensorFlow) to build your own deep learning models, of course, but having full access to these new servers will add dimension of flexibility to Google’s existing services that isn’t currently available on its platform.
While Google offers its service for building custom machine-learning models, it also provides developers with a number of pre-trained models for machine vision, speech-to-text conversion, translations and extracting information from text.
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