Amazon Machine Learning vs BigML: What are the differences?
Developers describe Amazon Machine Learning as "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. On the other hand, BigML is detailed as "Machine Learning, made simple. Predictive analytics for big data and not-so-big data". BigML provides a hosted machine learning platform for advanced analytics. Through BigML's intuitive interface and/or its open API and bindings in several languages, analysts, data scientists and developers alike can quickly build fully actionable predictive models and clusters that can easily be incorporated into related applications and services.
Amazon Machine Learning and BigML can be categorized as "Machine Learning as a Service" 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, BigML provides the following key features:
- REST API
- bindings in Pyton, Java, Ruby, node.js, C#, Clojure, PHP, and more
- several algorithms, including categorical & regression decision trees, ensembles of trees (random decision forest), cluster analysis and more