You can create online deployments for SPSS Modeler flows that use multiple input streams to provide data to the model.
Process overview
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When you use multiple input streams to create an SPSS Modeler flow, you can create an online deployment for your model.
The following graphic shows an SPSS Modeler flow that uses two input streams. The flow processes the data and creates a machine learning model. You can save this model and create an online deployment. After deploying the model, you can use the
endpoint for scoring with your applications.
Tasks for deploying multi-source SPSS Modeler flows
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You can create and deploy a multi-source SPSS Modeler flow from the user interface or watsonx.ai REST API.
Creating and deploying models from the user interface
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Follow these steps to create and deploy a multi-source SPSS Modeler flow from the user interface:
Create an SPSS Modeler flow: You can create a multi-source model in your Project by creating an SPSS Modeler flow.
Promote the model asset: After creating the model, promote the asset for you model from your Project to your deployment space.
Deploy the model: Create an online deployment to use the endpoint for scoring.
Creating and deploying multi-source models from the user interface
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Follow these steps to create multi-source models in SPSS Modeler and deploy the models from the user interface.
Task 1: Create a multi-source model in SPSS Modeler
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You can create a multi-source model by creating an SPSS Modeler flow. For more information, see Creating SPSS Modeler flows.
Task 2: Promote the model asset
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To deploy your mode, you must promote the model asset created by using SPSS Modeler to your deployment space. For more information, see Promoting assets to a deployment space.
Task 3: Deploying multi-source models
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Follow these steps to create an online deployment for a machine learning model that is created with multiple input sources in SPSS Modeler:
Prerequisites
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You must set up your task credentials for deploying your AI services. For more information, see Managing task credentials.
From your deployment space, go to the Assets tab.
For your machine learning model, click the Menu icon and select Deploy.
Select Online as the deployment type.
Enter a name for your deployment and optionally enter a serving name, description, and tags.
Click Create.
Task 4: Testing the deployed model
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You can score your deployed model by providing input in text or JSON format.
Follow these steps for scoring your deployed model:
In your deployment space or your project, open the Deployments tab and click the deployment name.
Click the Test tab to input prompt text and get a response from the deployed asset.
Enter test data in text or JSON format to generate output.
Creating and deploying multi-source SPSS Modeler flows with REST API
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Follow these steps to create multi-source models in SPSS Modeler and deploy the models from the watsonx.ai REST API.
Task 1: Creating a multi-source model with SPSS Modeler
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You can create a model with multiple input sources by using SPSS Modeler.
Requirements for model schema in JSON format
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When you create a model a multi-input model schema in JSON format, your schema must meet the following requirements:
Key requirements for input JSON objects
Component
Description
Structure
Each object must contain id, fields, and values.
ID
Must be a string.
Fields
Must be an array of strings.
Values
Must be an array of arrays.
Value items
Each item within values must be an array.
The following code shows how to create a model with schema:
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