BigML vs NanoNets: What are the differences?
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; NanoNets: Machine learning API with less data. Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application.
BigML and NanoNets can be categorized as "Machine Learning as a Service" tools.
Some of the features offered by BigML are:
- 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
On the other hand, NanoNets provides the following key features:
- Image categorization API with less than 30 images per category
- Custom object localization API
- Text deduplication API