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A quick comparison of Machine Learning platforms of Amazon, Microsoft, Google and IBM

A quick comparison of Machine Learning platforms of Amazon, Microsoft, Google and IBM

Gone are the days when a machine needed a precise set of instructions to complete a task. With the Machine Learning (ML) technology, today’s machines have become smarter. Now they can learn patterns, draw inferences and behave in a set manner.

Machine learning takes birth from pattern recognition and the idea that the machines can utilize data to learn by identifying patterns in it and eventually make predictions in the future based on the previously identified pattern, without being programmed. It can be better understood as augmented intelligence – a combination of human and machine capabilities.

It’s not a new concept, but has gained momentum recently. It actually dates to the 80s and 90s, though at that time, it was not highly advanced.

The advent of big and unstructured data, faster computational speed along with real-time solutions have opened a can of use cases for ML based applications.

With the help of various ML tools available in the market, one can easily start building their own working model, even with a small team. So, let’s look at some of the major machine learning platforms and compare their functionalities.

Machine learning platforms’ comparison:Amazon Vs Azure Vs Google Vs IBM

  1. Amazon Machine Learning Platform

    Amazon Machine Learning platform offers one of the most automated solutions to help built ML applications swiftly. The platform offers guided algorithms and wizards. With these guided tools, one can easily start building his ML models.

    Features:

    Amazon Machine Learning can load data from any of these three:

    • Amazon’s Simple Storage Services (S3),
    • Amazon Redshift or
    • MySQL databases in Amazon RDS (Relational Database Service).

    The data pre-processing operations are automatically carried-on without requiring any manual intervention.

    The visual tools help one in previewing the models and the APIs guide in creating them. Once a model is ready, Amazon’s in-built tools can be used to refine and fine-tune the data after which it can generate predictions.

    The user can later decide whether he wants to use batch APIs to get predictions for the entire data set at once, or use real time API to generate predictions on demand.

    Interface:

    • Amazon Command Line Interface
    • Amazon Machine Learning Console

    Benefits:

    • Quick and easy way to create ML models due to automation.
    • Offers scalability and hence can generate billions of predictions for user’s application.
    • There is no set-up cost. Hence, one can start using it instantly and pay per usage.
    • Proven technology used by Amazon itself.

    Drawbacks:

    • High-level automation

      For those who look for full automation, Amazon ML platform is perfect. But, it cannot be used to train entry level developers the economics of machine learning.

    • Limited Predictions

      Amazon ML platform has limited prediction capacity – binary classification, regression and multiclass classification.

    • Best fit for:

      High level automation gives speed to the entire process. Hence, it is fit for operations that have deadline constraints. The speed it offers will help build an application in seconds.

  2. Azure Machine Learning

    With Azure Machine Learning, one can build powerful applications based on cloud. It comes as a cloud-based fully managed platform which can be used to build and deploy predictive analytics solutions and even share them. Machine learning in Azure is based on applied methods. With a simple drag and drop interface, the user can go from idea to deployment in seconds.

    Features:

    Azure machine learning is for both experienced and new data scientists. As such, it gives room for performing tasks like data exploration, choosing methods, pre-processing validating etc. manually. Its graphical interface gives visual preview of each step in data building model for better results.
    Interface:

    • Drag-and-drop environment of Azure Machine Learning studio
    • Packages Python and R coding

    Benefits:

    • Ability to perform manual operations is good for learning the basics of ML.
    • It supports a huge variety of methods, over 100.
    • Supports multiple classifications like binary, multiclass, regression, recommendation, detection, text analysis and much more.
    • Cortana Intelligence gallery is an added benefit.

    Drawback:

    • Deep learning process

      It is not good for tasks that require quick implementation.

      Best fit for:

      It is a powerful tool for those who are just getting started with ML and need to develop a thorough understanding of this field.

  3. Google Prediction API

    With Google ML platform, one can quickly build powerful ML models that can work on any data type, of any size. The TensorFlow framework helps one create the models. Users can leverage the state-of-the-art algorithms used by Google itself for generating search results and other industry-leading applications.

    Features:

    Google Cloud Machine Learning Engine can use any tensor flow model to perform large scale training over a managed cluster. Its integration with Google Cloud Dataflow helps in pre-processing, while Google Cloud Storage allows one to easily access data from it. One can get Instant predictions using online and batch prediction services. User can HyperTune the models for automating the training. Users get automated algorithms suggestions as well.

    Interface:

    • Google Command Line to control TensorFlow procedures using gcloud ml- engine.

    Benefits:

    • Strong Integrations with other Google services like Cloud Data Flow, Cloud Storage and Cloud Datalab.
    • HyperTune the models to automatically detect the workable models.
    • Fully Managed service ensures that one spends more time on developing and less on resource provisioning and monitoring.
    • Portable models allow one to train the ML models locally on TensorFlow and download for local execution.

    Drawback:

    • Less pre-trained models

      The availability of pre-trained models is less when compared to Azure.

      Best Fit for:

      Google offers the perfect environment for running ML applications within tight deadlines. Much like Amazon, it is also automated.

  4. IBM Watson

    IBM offers machine learning capabilities through its Watson Analytics. Built on IBM’s proven analytics platform, the developers can easily work on data which must be stored in IBM Bluemix to build smart models and improve decision making. It is mostly focused on getting models into production with the help of REST API connectors.

    Features:

    Watson Interface has three components – Explore, Predict and Assemble. With Explore, the user can use available queries or type in new text. The Predict tool provides prediction on one or more target variables based on others. Assemble tool provides an interface to create workbooks. They can contain the presentation materials, reports, data visuals etc. It further provides a drag-and-drop interface to quickly create the models. However, there are no automated algorithms suggestion.

    Interface:

    • Graphical Analytics Software SPSS for front-end
    • API connectors

    Benefits:

    • Deep learning capabilities.
    • Strong data visualization and description of various data values.

    Drawbacks:

    • It is mostly targeted towards large organizations.
    • It does not process structured data directly.

    Best Fit for:

    Those who look forward to build machine learning platform backed applications with the help of API connectors.

    Conclusion:

    Which machine learning platform is best, depends on a user’s requirements. If what you look for is a comprehensive and fully automated ML platform then Amazon is the best fit, while IBM is good for a new data scientist who is just getting started with building ML applications. Azure, on the other hand, can manage both new and experienced data scientists.

    Let us know what you think of these machine learning platforms – which one are you using and which one among the above is best, per you.

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