From the what is page:
PredictionIO is an open-source Machine Learning server for developers and data scientists to build and deploy predictive applications in a fraction of the time.
PredictionIO template gallery offers a wide range of predictive engine templates for download, developers can customize them easily. The DASE architecture of engine is the “MVC for Machine Learning”. It enables developers to build predictive engine components with separation-of-concerns. Data scientists can also swap and evaluate algorithms as they wish. The core part of PredictionIO is an engine deployment platform built on top of Apache Spark. Predictive engines are deployed as distributed web services. In addition, there is an Event Server. It is a scalable data collection and analytics layer built on top of Apache HBase.
PredictionIO eliminates the friction between software development, data science and production deployment. It takes care of the data infrastructure routine so that your data science team can focus on what matters most.
The most attractive feature of PredictionIO is the ability to configure and test multiple engines with less overhead.
At the same time, I am not altogether sure that “…accelerat[ing] scalable machine learning infrastructure management” is necessarily a good idea.
You may want to remember that the current state of cyberinsecurity, where all programs are suspect and security software may add more bugs that it cures, is a result, in part, of shipping code because “it works,” and not because it is free (or relatively so) of security issues.
I am really not looking forward to machine learning uncertainty like we have cyberinsecurity now.
That isn’t a reflection on PredictionIO but the thought occurred to me because of the emphasis on accelerated use of machine learning.