DASE – A Design Pattern for Machine Lear...

In this talk, we will introduce the latest developments of open source machine learning in relation to Scala, Apache Spark, MLlib and PredictionIO. We will show how such tools can be used to build and deploy predictive engines in real production environments. Introducing the DASE design pattern, we will illustrate how developers, data engineers and data scientists can build machine learning applications with separation of concern (SoC) in mind. “D" stands for Data Source and Data Preparator, which take care of the preparation of data for model training. “A" stands for Algorithm, which is where the code of one or more algorithms are implemented with native supported for MLlib and other machine learning libraries. “S” stands for Serving, which handles the application logic during the retrieval of predicted results. Finally, “E” stands for Evaluation. Finally, we will cover upcoming developments, including new Engine Templates for various business scenarios.