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’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; 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
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.
What is Amazon Machine Learning?
What is rasa NLU?
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What tools integrate with Amazon Machine Learning?
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