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Amazon Machine Learning

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RAML

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Amazon Machine Learning vs RAML: What are the differences?

What is Amazon Machine Learning? Visualization tools and wizards that guide you through the process of creating ML models w/o having to learn complex ML algorithms & technology. This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.

What is RAML? RESTful API Modeling Language (RAML) makes it easy to manage the whole API lifecycle from design to sharing. RESTful API Modeling Language (RAML) makes it easy to manage the whole API lifecycle from design to sharing. It's concise - you only write what you need to define - and reusable. It is machine readable API design that is actually human friendly.

Amazon Machine Learning belongs to "Machine Learning as a Service" category of the tech stack, while RAML can be primarily classified under "API Tools".

Some of the features offered by Amazon Machine Learning are:

  • Easily Create Machine Learning Models
  • From Models to Predictions in Seconds
  • Scalable, High Performance Prediction Generation Service

On the other hand, RAML provides the following key features:

  • Create and pull in namespaced, reusable libraries, containing data types
  • Annotations let you add vendor specific functionality without compromising your spec
  • Traits and resource Types let you take advantage of code reuse and design patterns

According to the StackShare community, Amazon Machine Learning has a broader approval, being mentioned in 9 company stacks & 10 developers stacks; compared to RAML, which is listed in 9 company stacks and 6 developer stacks.

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Pros of Amazon Machine Learning
Pros of RAML
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    • 11
      API Specification
    • 7
      Human Readable
    • 6
      API Documentation
    • 3
      Design Patterns & Code Reuse
    • 2
      API Modeling
    • 2
      Automatic Generation of Mule flow
    • 2
      Unit Testing
    • 1
      SDK Generation
    • 1
      API Mocking

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    What is Amazon Machine Learning?

    This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.

    What is RAML?

    RESTful API Modeling Language (RAML) makes it easy to manage the whole API lifecycle from design to sharing. It's concise - you only write what you need to define - and reusable. It is machine readable API design that is actually human friendly.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Amazon Machine Learning?
    What companies use RAML?
    See which teams inside your own company are using Amazon Machine Learning or RAML.
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    What tools integrate with Amazon Machine Learning?
    What tools integrate with RAML?
      No integrations found
      What are some alternatives to Amazon Machine Learning and RAML?
      TensorFlow
      TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
      Apache Spark
      Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
      Amazon SageMaker
      A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
      RapidMiner
      It is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment.
      Azure Machine Learning
      Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.
      See all alternatives