Cloud Based Machine Learning

Our dedicated team offers you modern machine learning services, with predefined models and opportunity to create your personalised Ml models. Practised with big data our intelligent systems has better performance and increased accuracy compared to other large scale self learning systems. Our services are fast, scalable and easy to use. Available as a cloud service we bring you new opportunities and speed to your business applications.

With cloud based machine learning, you don’t have to invest upfront on hardware or software, and you pay as you go, so you can initiate small and scale as your application grows.

The four main vendors for cloud based Machine Learning are Amazon, Google, Microsoft and IBM. Each have their own strengths and weaknesses and can enhance the quality of your decision making.

Machine Learning – Amazon

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Amazon Machine Learning platform offers ready-made and easily accessible prediction models for any developer, even if they do not have a data science background. Fueled by technology that powers its internal algorithms, these models can geneFeaturesrate millions of predictions either in batches or in real-time. In addition to this, in the 2016 re:Invent developer conference, it announced additional offerings in Image Recognition, Text-to-Speech Service and Speech Recognition, bringing its offerings at par with its competitors. A pay-as-you-go model, requiring little investment in hardware or software, has made Amazon one of the best ML platform providers a newbie can sign up for.


  • It uses the Amazon Machine Learning Console and Amazon Command Line Interface.
  • Data needs to be stored in an AWS account such as S3, Redshift and RDS.
  • It works on a pay-as-you-go model, and for a thousand batch predictions it costs as little as 10 cents.
  • It has pre-built algorithms trained to perform regression analysis and classification (binary and multiclass).

Machine Learning – Google Cloud

Google prides itself on being an AI-first company. Almost of all of Google’s marquee products use advanced machine learning models and AI capabilities, including speech recognition, image recognition and natural language processing. This makes Google Cloud Machine Learning platform a powerful tool for the beginner as well as the expert. It sports a mix of pre-trained models, besides allowing users to build their own models. It supports video analysis, image recognition, text analysis and translation services.


  • It uses the Google Cloud ML Engine Interface.
  • Tensor Flow is the machine learning library of Google, an open source platform that lets more serious developers create their own models.
  • For faster deployment of simpler models, Google offers a prediction API through the REST API interface.
  • A Google Cloud account is required to store the data.
  • It also uses a pay-as-you-go model and charges about 10 cents for a thousand batch predictions.

Machine Learning – Microsoft Azure


  • It uses the Azure Machine Learning Studio as its interface, letting you build models in a drag-and-drop environment.
  • It provides automated algorithms to run decision trees, deep neural networks, classification and regression.
  • While large data sets (of over 2 GB) must be housed in the Azure Cloud, it does allow smaller data sets to be uploaded from other service providers.
  • While there is a free version with limited features for personal use, the standard version comes at $9.99 per user and there is a $1 fee per hour of experimentation .
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IBM Watson Machine Learning

Named after the company’s founder, Thomas J Watson, the IBM Watson achieved fame and limelight with its 2011 Jeopardy win against two of its greatest champions. From there, the process had begun to turn it into the machine learning behemoth it is today. Watson allows a user to search for algorithms and queries, use a prediction tool to give predictions, and assemble tool to create workbooks. It enables powerful data visualizations and allows easy creation of models with its drag and drop interface.


  • It uses the SPSS Graphical Analytics Software as a front-end interface.
  • The data must be housed and predictions run in IBM Bluemix.
  • Focused on its enterprise clients, the service enables creating ML based applications through API connectors.
  • There are paid as well as free versions available.

IBM views AI and machine learning as ‘augmented intelligence’ to enhance quality decision-making. IBM’s APIs are being put to use in areas such as retail or finance, but their core area of focus is in medicine. While it has deep learning capabilities with data visualizations, it is primarily meant for large organizations.