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Amazon Machine Learning

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Sphinx

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Amazon Machine Learning vs Sphinx: 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 Sphinx? Open source full text search server, designed from the ground up with performance, relevance (aka search quality), and integration simplicity in mind. Sphinx lets you either batch index and search data stored in an SQL database, NoSQL storage, or just files quickly and easily — or index and search data on the fly, working with Sphinx pretty much as with a database server. A variety of text processing features enable fine-tuning Sphinx for your particular application requirements, and a number of relevance functions ensures you can tweak search quality as well.

Amazon Machine Learning can be classified as a tool in the "Machine Learning as a Service" category, while Sphinx is grouped under "Search Engines".

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, Sphinx provides the following key features:

  • Output formats: HTML (including Windows HTML Help), LaTeX (for printable PDF versions), ePub, Texinfo, manual pages, plain text
  • Extensive cross-references: semantic markup and automatic links for functions, classes, citations, glossary terms and similar pieces of information
  • Hierarchical structure: easy definition of a document tree, with automatic links to siblings, parents and children

According to the StackShare community, Sphinx has a broader approval, being mentioned in 38 company stacks & 14 developers stacks; compared to Amazon Machine Learning, which is listed in 9 company stacks and 10 developer stacks.

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Pros of Amazon Machine Learning
Pros of Sphinx
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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 Sphinx?

    It lets you either batch index and search data stored in an SQL database, NoSQL storage, or just files quickly and easily — or index and search data on the fly, working with it pretty much as with a database server.

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

    What companies use Amazon Machine Learning?
    What companies use Sphinx?
    See which teams inside your own company are using Amazon Machine Learning or Sphinx.
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    What tools integrate with Amazon Machine Learning?
    What tools integrate with Sphinx?
      No integrations found

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      What are some alternatives to Amazon Machine Learning and Sphinx?
      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