Score Spark-built machine learning models

Introduction This topic describes how to load machine learning (ML) models that have been built using Spark MLlib and stored in Azure Blob Storage (WASB), and how to score them with datasets that have also been stored in WASB. It shows how to pre-process the input data, transform features using the indexing and encoding functions in the MLlib toolkit, and how to create a labeled point data object that can be used as input for scoring with the ML models. The models used for scoring include Linear Regression, Logistic Regression, Random Forest Models, and Gradient Boosting Tree Models. Prerequisites You need an Azure account and an HDInsight Spark cluster to begin this walkthrough. See the Overview of Data Science using Spark on Azure HDInsight for these requirements, for a description…

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