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Said to “eliminate the friction between software development, data science and production deployment,” PredictionIO is an open-source Machine Learning server for developers and data scientists to build and deploy predictive applications.

The core part of the tool is an engine deployment platform built on top of Apache Spark.

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. 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. It takes care of the data infrastructure routine so that your data science team can focus on what matters most.

The template gallery offers a wide range of predictive engine templates for download and developers can customize them easily.

Data scientists use the tool to evaluate models and keep track of parameter adjustment history. With the DASE architecture, scientists can use or develop re-usable components such as data sampling methods and evaluation metrics. PredictionIO also helps unify different type of data from multiple platforms and provide an interface for data analysis. Data scientists can look at the data using their favorite analytical tools such as Tableau, IPython Notebook and Zeppelin.




Salesforce has made another acquisition to build out its technology in machine learning and big data analytics: the company has acquired PredictionIO, a startup based out of Palo Alto that had developed an open source-based machine learning server.

Originally called TappingStone when it was founded in 2012 in London, the startup at first had built a product that it described as “Machine Learning as a Service” before pivoting to an open source model.

Under that new direction, the company had developed some traction, with some 8,000 developers and 400 apps powered by its technology. As a point of reference, that’s double the number of developers in the community as of 2014.


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