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02_​Mass_​Learning_​Event_​Prediction_​MLlib_​to_​PMML

MLlib model to PMML

This workflow demonstrates the usage of the Spark MLlib to PMML node. Together with the Compiled Model Predictor and the JSON Input/Output node it can be used to model a so called lambda architecture which learns a machine learning model at scale on historical data offline and predicts events online using the learned model.

The workflow makes use of the Create Local Big Data Environment node to create a Spark context. You can swap this node out for a Create Spark Context (Livy) node to connect to a remote cluster.

Mass Learning on all Your Historical Data Prediction Fast Event Prediction Mass Learning Event Prediction This workflow demonstrates the usage of the Spark MLlib to PMML node. train modelin SparkconvertsPMML toJavatraining datatest dataKNIME tableto DataFrameInstantiate Sparklocally ContainerInput (JSON) ContainerOutput (JSON) Spark k-Means Spark MLlib to PMML PMML Compiler Compiled ModelPredictor JSON to Table Table to JSON File Reader File Reader Entropy Scorer Compiled ModelPredictor Table to Spark Create Local BigData Environment Mass Learning on all Your Historical Data Prediction Fast Event Prediction Mass Learning Event Prediction This workflow demonstrates the usage of the Spark MLlib to PMML node. train modelin SparkconvertsPMML toJavatraining datatest dataKNIME tableto DataFrameInstantiate Sparklocally ContainerInput (JSON) ContainerOutput (JSON) Spark k-Means Spark MLlib to PMML PMML Compiler Compiled ModelPredictor JSON to Table Table to JSON File Reader File Reader Entropy Scorer Compiled ModelPredictor Table to Spark Create Local BigData Environment

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Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: koetter
Created at: 2015-05-27
On NodePit since: 2019-07-12
Last update: 2024-10-06
Created with KNIME version: v4.1.0
Tags: SparkHadoopBig Data

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