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03_​Snowflake_​Model_​Deployment

Customer Churn Predictor Deployment with KNIME and Snowflake
Customer Churn Predictor Deployment with KNIME and SnowflakeThis workflow is an example of how to deploy a customer churn predictor as Snowflake User-Defined Function. This way the prediction is pushed downinto Snowflake were the data resides. The prediction result is persistet as new database table in Snowflake.Example data provided from kaggle: https://www.kaggle.com/becksddf/churn-in-telecoms-dataset 1. Selection.Selects the "calls_contracts" table to use for prediction.If you haven't executed the 01b_Snowflake_ETLworkflow before you can use the upper branch to createthe "calls_contracts" table in Snowflake with exampledata. 2. Deployment and Execution. Convert the customer churn prediction model into aUser-Defined Function in Snowflake. 3. Persisting.The prediction result can either be readback into KNIME or persisted as newdatabase table within Snowflake. Read new data Change connectioninformation to your Snowflake accountApply H2O modelat scale in SnowflakeRead the learned modelCreate db table with dataSelect existingtable withnew dataPersist prediction result in SnowflakeRead datainto KNIMECSV Reader Snowflake Connector Snowflake H2O MOJOPredictor (Classification) H2O MOJO Reader DB Writer DB Table Selector DB ConnectionTable Writer DB Reader Customer Churn Predictor Deployment with KNIME and SnowflakeThis workflow is an example of how to deploy a customer churn predictor as Snowflake User-Defined Function. This way the prediction is pushed downinto Snowflake were the data resides. The prediction result is persistet as new database table in Snowflake.Example data provided from kaggle: https://www.kaggle.com/becksddf/churn-in-telecoms-dataset 1. Selection.Selects the "calls_contracts" table to use for prediction.If you haven't executed the 01b_Snowflake_ETLworkflow before you can use the upper branch to createthe "calls_contracts" table in Snowflake with exampledata. 2. Deployment and Execution. Convert the customer churn prediction model into aUser-Defined Function in Snowflake. 3. Persisting.The prediction result can either be readback into KNIME or persisted as newdatabase table within Snowflake. Read new dataChange connectioninformation to your Snowflake accountApply H2O modelat scale in SnowflakeRead the learned modelCreate db table with dataSelect existingtable withnew dataPersist prediction result in SnowflakeRead datainto KNIMECSV Reader Snowflake Connector Snowflake H2O MOJOPredictor (Classification) H2O MOJO Reader DB Writer DB Table Selector DB ConnectionTable Writer DB Reader

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

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Created by: rosy
Created at: 2015-04-23
On NodePit since: 2022-04-06
Last update: 2024-11-17
Created with KNIME version: v4.5.2
Tags: SnowflakeCustomer IntelligenceCIChurnMarketing AnalyticsH2O Machine LearningMOJOsDBDatabase

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