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Training_​workflow

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Training workflow. This workflow accesses the training data from a file, splits and preprocesses it, and trains the credit scoring model.
- Additionally, it exports the captured and created prediction workflow to CDDS.

Step 1. Access the training data from the databaseConnect to the database*Provide the hostname and database name shared by the trainers aswell as your username (userXX), password (userXX), and schema(userXX)customer_data table will be read from the database and the trainingdata will be selected* Why database instead of a file here? This workflow will be executedon KNIME Business Hub and the data should be accessible fromthere Session 3 - Continuous Deployment for Data ScienceExercise 02 CDDS Automated deployment Learning objective: In this part of the exercise you need to finalize the training workflow.Workflows description: Training workflow. This workflow accesses the training data from a file, splits and preprocesses it, and trains the credit scoring model.Additionally, it exports the captured and created prediction workflow to CDDS.You'll find the instructions to the exercises in the yellow annotations. Step 2. Explicitly define both input andoutput nodes of the captured workflowAdd the Container Output (Table) nodebefore the Capture Workflow End node*.* We don't use Workflow Writer nodeanymore to add the output container nodeautomatically, therefore, it should beexplicitly defined and configured. Step 3. Expose the captured prediction workflow tothe CDDSExpose the captured prediction workflow to theCDDS with the Workflow Service Output node:Connect the Captured Workflow Port Object to theWorkflow Service Output node*.Reset the workflow* Workflow Service Output node functions in thesame way as a Container Output nodes - with thedifference that it corresponds to the Call WorkflowService node. The Workflow Service nodes are usedby the CDDS. 75% training 25% testProvide the DB parameters,your user & password and schema CaptureWorkflow End ContainerInput (Table) CaptureWorkflow Start Partitioning Training data PostgreSQLConnector XGBoost TreeEnsemble Learner XGBoost Predictor Data Preprocessing(Apply) Data Preprocessing Step 1. Access the training data from the databaseConnect to the database*Provide the hostname and database name shared by the trainers aswell as your username (userXX), password (userXX), and schema(userXX)customer_data table will be read from the database and the trainingdata will be selected* Why database instead of a file here? This workflow will be executedon KNIME Business Hub and the data should be accessible fromthere Session 3 - Continuous Deployment for Data ScienceExercise 02 CDDS Automated deployment Learning objective: In this part of the exercise you need to finalize the training workflow.Workflows description: Training workflow. This workflow accesses the training data from a file, splits and preprocesses it, and trains the credit scoring model.Additionally, it exports the captured and created prediction workflow to CDDS.You'll find the instructions to the exercises in the yellow annotations. Step 2. Explicitly define both input andoutput nodes of the captured workflowAdd the Container Output (Table) nodebefore the Capture Workflow End node*.* We don't use Workflow Writer nodeanymore to add the output container nodeautomatically, therefore, it should beexplicitly defined and configured. Step 3. Expose the captured prediction workflow tothe CDDSExpose the captured prediction workflow to theCDDS with the Workflow Service Output node:Connect the Captured Workflow Port Object to theWorkflow Service Output node*.Reset the workflow* Workflow Service Output node functions in thesame way as a Container Output nodes - with thedifference that it corresponds to the Call WorkflowService node. The Workflow Service nodes are usedby the CDDS. 75% training 25% testProvide the DB parameters,your user & password and schemaCaptureWorkflow End ContainerInput (Table) CaptureWorkflow Start Partitioning Training data PostgreSQLConnector XGBoost TreeEnsemble Learner XGBoost Predictor Data Preprocessing(Apply) Data Preprocessing

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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: Lada.Rudnitckaia
Created at: 2022-10-07
On NodePit since: 2023-10-05
Last update: 2024-04-26
Created with KNIME version: v5.1.0
Tags: EducationIntegrated DeploymentMachine LearningModel DeploymentCredit ScoringKNIME Business HubRESTful ServiceServiceModel as a ServiceService DeploymentCDDSAutomated Deployment

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