Open Source Summit + ELC Europe 2018

The common perception of machine learning is that it starts with data and ends with a model. In real-world production systems, the traditional data science and machine learning workflow of data preparation, feature engineering and model selection, while important, is only one aspect. A critical missing piece is the deployment and management of models, as well as the integration between the model creation and deployment phases.

In this talk I will introduce the Portable Format for Analytics (PFA) for portable, open and standardized deployment of data science pipelines & analytic applications. I will also cover open-source work we have done to support export of Apache Spark ML pipelines to PFA, as well as explore PFA export for other popular ML frameworks (such as scikit-learn). Finally, I will compare and contrast PFA to other available alternatives including PMML, MLeap, ONNX and Apple’s CoreML.