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Google Cloud Platform will be getting GPU-centric machines in early 2017

Google has come up with advanced features to Cloud Machine Learning service which was released in the beginning of this year. It is also considered as one of the ”fastest growing product areas.” The new features have been introduced for Cloud Machine Learning users and developers where they can run their own machine learning workloads in Google’s cloud. Google does not allow its developers to access virtual machines with high-end graphics processing units (GPUs) like other competitors. The specialized workloads in machine learning are highly dependent on GPUs which increases the capacity of the core algorithms that has made this technique so strong. The service is combined with Google Cloud Dataflow for preprocessing which allows you to access data from Google Cloud Storage and others Developers and users have to be patient to get started with working on their own machine-learning workloads on the Google Cloud Platform. The latest GPU-centric machines will be launched in early 2017.Users can still use the existing Cloud Machine Learning services  to develop your own deep machine learning models, and it is added advantage  with the new feature where they can access the new servers which were not given earlier. This new feature adds the important aspect of flexibility to the Google’s existing services that are not available with the current platform. Machine learning can now be of any data and any size. Its latest features are given below: •    Integrated •    HyperTune •    Managed Service •    Scalable Service •    Notebook Developer Experience •    Portable Models Google is offering its service for developing customized machine-learning models; it also supports developers with a number of pre-trained models for text-to-speech conversion, machine vision, extracting information from text and translations. The advancement in machine learning has helped in building its own customized chips. Google Platform pricing has been a reduction of the price by 80 percent for using Cloud Vision API .Additional to this, the service works well in identifying company logos, landmark, and other objects.
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Google Cloud Platform will be getting GPU-centric machines in early 2017

Susan May
What's New
16th Nov, 2016
Google Cloud Platform will be getting GPU-centric machines in early 2017

Google has come up with advanced features to Cloud Machine Learning service which was released in the beginning of this year. It is also considered as one of the ”fastest growing product areas.” The new features have been introduced for Cloud Machine Learning users and developers where they can run their own machine learning workloads in Google’s cloud.

Google does not allow its developers to access virtual machines with high-end graphics processing units (GPUs) like other competitors. The specialized workloads in machine learning are highly dependent on GPUs which increases the capacity of the core algorithms that has made this technique so strong. The service is combined with Google Cloud Dataflow for preprocessing which allows you to access data from Google Cloud Storage and others

Developers and users have to be patient to get started with working on their own machine-learning workloads on the Google Cloud Platform. The latest GPU-centric machines will be launched in early 2017.Users can still use the existing Cloud Machine Learning services  to develop your own deep machine learning models, and it is added advantage  with the new feature where they can access the new servers which were not given earlier. This new feature adds the important aspect of flexibility to the Google’s existing services that are not available with the current platform.

Machine learning can now be of any data and any size. Its latest features are given below:

•    Integrated

•    HyperTune

•    Managed Service

•    Scalable Service

•    Notebook Developer Experience

•    Portable Models

Google is offering its service for developing customized machine-learning models; it also supports developers with a number of pre-trained models for text-to-speech conversion, machine vision, extracting information from text and translations. The advancement in machine learning has helped in building its own customized chips. Google Platform pricing has been a reduction of the price by 80 percent for using Cloud Vision API .Additional to this, the service works well in identifying company logos, landmark, and other objects.

Susan

Susan May

Writer, Developer, Explorer

Susan is a gamer, internet scholar and an entrepreneur, specialising in Big Data, Hadoop, Web Development and many other technologies. She is the author of several articles published on Zeolearn and KnowledgeHut blogs. She has gained a lot of experience by working as a freelancer and is now working as a trainer. As a developer, she has spoken at various international tech conferences around the globe about Big Data.


Website : https://www.zeolearn.com

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