Amazon SageMaker vs BigML: What are the differences?
Amazon SageMaker: Accelerated Machine Learning. A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale; BigML: 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 SageMaker and BigML can be primarily classified as "Machine Learning as a Service" tools.
Some of the features offered by Amazon SageMaker are:
- Build: managed notebooks for authoring models, built-in high-performance algorithms, broad framework support
- Train: one-click training, authentic model tuning
- Deploy: one-click deployment, automatic A/B testing, fully-managed hosting with auto-scaling
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
What is Amazon SageMaker?
What is BigML?
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