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.