GOOGLE Cloud Machine Learning Engine in Artificial Intelligence Platform

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Google Cloud Platform provides cloud computing services that include machine learning, data analytics, and data storage. TensorFlow, an open-source toolkit from Google, may be used to do numerical calculations using data flow graphs. TensorFlow assists users from a variety of industries in using a variety of applications, ranging from language translation to illness early detection. Aside from that, Google's ML Engine allows developers to design machine learning models on any size of data. In addition, the company's Cloud Machine Learning Engine provides managed services that eliminate the need for infrastructure and serve as a foundation for model creation and prediction.

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Summary

For improved product development, Google Cloud Platform provides safe and cost-effective infrastructure, as well as data and analytics capabilities. In addition, the Google Cloud ML Engine offers training and prediction services that can be used together or separately. Enterprises have used it to tackle challenges as diverse as recognizing clouds in satellite photos, assuring food safety, and replying four times faster to consumer emails. AI Platform Training and AI Platform Prediction are the new names for the training and prediction services in ML Engine.

Demo

For more information and a demo, the users can drop a request at the site and get across the demo soon.

Pricing

Its pricing is not mentioned on the website. You can contact them through email or phone for an understanding of the functionalities of this tool. Its pricing varies based on your need, and you can call them for a quote.

Features

Custom container support - Google can run any other framework on Cloud ML Engine along with native support for popular frameworks like TensorFlow. It simply uploads a Docker container with the training program and Cloud ML Engine that put it to work on Google's infrastructure.

Distributed training - Cloud ML Engine automatically sets up an environment for XGBoost and TensorFlow to run on multiple machines. It gets the speed that is needed by adding multiple GPUs to the training job or splitting it across multiple VMs.

Automatic resource provisioning - Cloud ML Engine is a managed service that automates all resource provisioning and monitoring builds models using managed distributed training infrastructure that supports CPUs, GPUs, and TPUs; and accelerates model development by training across many nodes or running multiple experiments in parallel.

HyperTune - It achieves quick results by automatically tuning deep learning hyperparameters with HyperTune. HyperTune saves many hours of tedious and error-prone work.

Portable models - The open-source TensorFlow SDK or other supported ML frameworks train models locally on sample datasets and use the Google Cloud Platform for training at scale. Models trained using Cloud ML Engine can be downloaded for local execution or mobile integration. It can also import sci-kit-learn, XGBoost, Keras, and TensorFlow models that have been trained anywhere for fully-managed, real-time prediction hosting.

Strengths
  • +6
    Solutions Offered
  • +14
    No. of Innovations
  • +6
    R&D Spend
  • +10
    Brand Recognition
  • +8
    Customer testimonials
  • +8
    Cloud
  • +14
    Managed Services
  • +9
    On-Premise
  • +5
    Professional Services
  • +7
    New Product Launches
  • +6
    Product Upgradation
  • +13
    Full Time Equivalent
  • +8
    Large Enterprises (Revenue> 500 Million)
  • +11
    Machine Learning
  • +7
    Medium sized enterprises
  • +10
    Natural Language Processing
  • +6
    Small Enterprise (Revenue< 100 Million)
  • +14
    Subscription / Licensing
  • +14
    Artificial creativity
  • +9
    Chatbots
Google Presence in Artificial Intelligence Platform
Google is continuously making efforts to lead the competitive AI platform market. The company is focused on the advancement of AI capabilities and aims at integrating the capabilities in its products and services, along with advancements in research, developments, acquisitions, and technologies. For instance, in May 2017, it released the second generation Tensor Processor Unit (TPU) to expedite the ML tasks and create more ML models. Furthermore, Google is focused on the development of AI-first data centers. Additionally, in the AI platform market, Google is designing AI tools for molecule discovery and analysis of images for use in the medical field. Apart from these strategies, Google follows the acquisition strategy. It has acquired various startups, such as DNNresarch, DeepMindTechnologies, Moodstcok, HalliLabs, and Api.ai, to strengthen its position in the competitive AI platform market.
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