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Amazon Machine Learning
Amazon Machine Learning

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rasa NLU
rasa NLU

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Amazon Machine Learning vs rasa NLU: What are the differences?

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鈥檝e 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鈥檛 have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure; rasa NLU: Open source, drop-in replacement for NLP tools like wit.ai. rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.

Amazon Machine Learning can be classified as a tool in the "Machine Learning as a Service" category, while rasa NLU is grouped under "NLP / Sentiment Analysis".

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, rasa NLU provides the following key features:

  • open source
  • python
  • NLP

rasa NLU is an open source tool with 5.76K GitHub stars and 1.7K GitHub forks. Here's a link to rasa NLU's open source repository on GitHub.

- No public GitHub repository available -

What is Amazon Machine Learning?

This new AWS service helps you to use all of that data you鈥檝e 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鈥檛 have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.

What is rasa NLU?

rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.
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          What are some alternatives to Amazon Machine Learning and rasa NLU?
          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
          Decisions about Amazon Machine Learning and rasa NLU
          Julien DeFrance
          Julien DeFrance
          Full Stack Engineering Manager at ValiMail | 2 upvotes 12K views
          atSmartZipSmartZip
          Amazon SageMaker
          Amazon SageMaker
          Amazon Machine Learning
          Amazon Machine Learning
          AWS Lambda
          AWS Lambda
          Serverless
          Serverless
          #FaaS
          #GCP
          #PaaS

          Which #IaaS / #PaaS to chose? Not all #Cloud providers are created equal. As you start to use one or the other, you'll build around very specific services that don't have their equivalent elsewhere.

          Back in 2014/2015, this decision I made for SmartZip was a no-brainer and #AWS won. AWS has been a leader, and over the years demonstrated their capacity to innovate, and reducing toil. Like no other.

          Year after year, this kept on being confirmed, as they rolled out new (managed) services, got into Serverless with AWS Lambda / FaaS And allowed domains such as #AI / #MachineLearning to be put into the hands of every developers thanks to Amazon Machine Learning or Amazon SageMaker for instance.

          Should you compare with #GCP for instance, it's not quite there yet. Building around these managed services, #AWS allowed me to get my developers on a whole new level. Where they know what's under the hood. Where they know they have these services available and can build around them. Where they care and are responsible for operations and security and deployment of what they've worked on.

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          How developers use Amazon Machine Learning and rasa NLU
          Avatar of Taylor Host
          Taylor Host uses Amazon Machine LearningAmazon Machine Learning

          Mild re-training data usage.

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