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3. Click the existing **URL placeholder key**, and then configure the following settings:
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3. Click the existing **URL placeholder**,(`id`) and then configure the following settings:
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| Field | Value |
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### Test data sources
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1. To now test that the HTTPS is configured and able to fetch information, click **Test**.
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1. To now test that the data resource is configured properly and able to fetch information, click the **Test** tab.
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The test now runs, displaying a Test API call response. In this response, you can see information about the confectionary. This includes the product categories, allergen information, and the brand who manufactured the product.
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The test now runs, displaying a test API call response. In this response, you can see information about the product. This includes the product categories, allergen information, and the brand who manufactured the product.
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Click **Save**.
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Click **Save Data Entity** (bottom right), and then click **Save** (upper right).
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# Use the Invoice Object Recommendation Business Blueprint to Train a Machine Learning Model
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<!-- description --> Train a machine learning model for the Data Attribute Recommendation service, using the Invoice Object Recommendation business blueprint.
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# Use the Invoice Object Recommendation (IOR) Business Blueprint to Train a Machine Learning Model
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<!-- description --> Train a machine learning model for the Data Attribute Recommendation service, using the Invoice Object Recommendation (IOR) business blueprint.
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## You will learn
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- How to train a machine learning model using the Invoice Object Recommendation business blueprint
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- How to train a machine learning model using the Invoice Object Recommendation (IOR) business blueprint
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- How to deploy a machine learning model to get financial object predictions
In the service key you created for Data Attribute Recommendation in the previous tutorial: [Use Free Tier to Set Up Account for Data Attribute Recommendation and Get Service Key](cp-aibus-dar-booster-free-key) or [Use Trial to Set Up Account for Data Attribute Recommendation and Get Service Key](cp-aibus-dar-booster-key), you find a section called `swagger` (as highlighted in the image below) with three entries, called `dm` (data manager), `mm` (model manager) and `inference`.
For this tutorial, copy the URL of the Swagger UI for `mm` and open it in a browser tab. The Swagger UI for the model manager allows you to train a machine learning model, to delete it, to deploy the model as well as to `undeploy` the model.
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>After finishing this tutorial, keep the Swagger UI for `mm` open to perform the clean up tasks in [Use the Invoice Object Recommendation Business Blueprint to Predict Financial Objects](cp-aibus-dar-swagger-ior-predict).
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>After finishing this tutorial, keep the Swagger UI for `mm` open to perform the clean up tasks in [Use the Invoice Object Recommendation (IOR) Business Blueprint to Predict Financial Objects](cp-aibus-dar-swagger-ior-predict).
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1. To be able to use the Swagger UI endpoints, you need to authorize yourself. In the top right corner, click **Authorize**.
2. Get the `access_token` value created in the previous tutorial: [Get OAuth Access Token for Data Attribute Recommendation Using Any Web Browser](cp-aibus-dar-web-oauth-token), then add **Bearer** (with capitalized "B") in front of it, and enter in the **Value** field.
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To train a machine learning model using the data that you uploaded in [Use an Invoice Object Recommendation Dataset Schema to Upload Training Data to Data Attribute Recommendation](cp-aibus-dar-swagger-ior-upload), you create a training job.
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To train a machine learning model using the data that you uploaded in [Use an Invoice Object Recommendation (IOR) Dataset Schema to Upload Training Data to Data Attribute Recommendation](cp-aibus-dar-swagger-ior-upload), you create a training job.
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With each training job you provide a model template or a business blueprint which combines data processing rules and machine learning model architecture. You can find the list of available model templates [here](https://help.sap.com/docs/Data_Attribute_Recommendation/105bcfd88921418e8c29b24a7a402ec3/1e76e8c636974a06967552c05d40e066.html). The only business blueprint currently available is Invoice Object Recommendation, as you can see [here](https://help.sap.com/docs/Data_Attribute_Recommendation/105bcfd88921418e8c29b24a7a402ec3/091eace025e14793be0e83ef2109b349.html).
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With each training job you provide a model template or a business blueprint which combines data processing rules and machine learning model architecture. You can find the list of available model templates [here](https://help.sap.com/docs/Data_Attribute_Recommendation/105bcfd88921418e8c29b24a7a402ec3/1e76e8c636974a06967552c05d40e066.html), and the list of available business blueprints [here](https://help.sap.com/docs/Data_Attribute_Recommendation/105bcfd88921418e8c29b24a7a402ec3/091eace025e14793be0e83ef2109b349.html).
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The Invoice Object Recommendation business blueprint that you use in this tutorial is suited to assign G/L (general ledger) accounts and other financial objects to incoming invoices without a purchase order reference.
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The Invoice Object Recommendation (IOR) business blueprint that you use in this tutorial is suited to assign G/L (general ledger) accounts and other financial objects to incoming invoices without a purchase order reference.
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To create the training job, proceed as follows:
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1. Expand the endpoint **POST /jobs** by clicking on it. Then click **Try it out**.
2. In the text area, replace the parameter value for `datasetId` with the `id` of your dataset that you have created in [Use an Invoice Object Recommendation Dataset Schema to Upload Training Data to Data Attribute Recommendation](cp-aibus-dar-swagger-ior-upload). Delete the `modelTemplateId` line from the **Request body**. Replace the parameter value `modelName` with your model name, `ior_tutorial_model`, for example. Make sure the parameter value for `businessBlueprintId` is `4788254b-0bad-4757-a67f-92d5b55f322d`. Click **Execute** to create the training job.
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2. In the text area, replace the parameter value for `datasetId` with the `id` of your dataset that you have created in [Use an Invoice Object Recommendation (IOR) Dataset Schema to Upload Training Data to Data Attribute Recommendation](cp-aibus-dar-swagger-ior-upload). Delete the `modelTemplateId` line from the **Request body**. Replace the parameter value `modelName` with your model name, `ior_tutorial_model`, for example. Make sure the parameter value for `businessBlueprintId` is `4788254b-0bad-4757-a67f-92d5b55f322d`. Click **Execute** to create the training job.
3. In the response of the service, you find the `id` of your training job. Copy the `id` as you'll need it in the next step. Along side the `id`, you find the training job's current status. Initially, the status is `PENDING` which says that the training job is in queue but has not started yet.
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1. Expand the endpoint `GET /jobs/{jobId}` by clicking on it. Then click **Try it out**.
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2. Fill the parameter `jobId` with `id` of your training job that you copied in the previous step. Click **Execute**.
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3. In the response, you find again the current status of your training job along with other details. Immediately after creation of the training job, the status is `PENDING`. Shortly after, it changes to `RUNNING` which means that the model is being trained. The training of the sample data usually takes about 5 minutes to complete but may run longer, up to a few hours due to limited availability of resources in the free tier environment. You can check the status every now and then. Once training is finished, the status changes to `SUCCEEDED` which means the service has created a machine learning model and you can proceed.
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You have successfully trained a machine learning model.
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1. Expand the endpoint `POST /deployments` by clicking on it. Then click **Try it out**.
3. In the response of the service, you find the `id` of the deployment and its status. Initially, the status is `PENDING`, indicating the deployment is in progress. Make sure to copy the `id` as you need it in the next step.
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1. Expand the endpoint `GET /deployments/{id}` by clicking on it. Then click **Try it out**.
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2. Fill the parameter `deploymentId` with the `id` of your deployment that you copied in the previous step. Click **Execute**.
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3. In the response of the service, you find the current status of the deployment. If the status is `SUCCEEDED`, your deployment is done. If the status is still `PENDING`, check back in a few minutes.
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You have successfully trained a machine learning model and deployed it. Next, you'll use your model to make predictions.
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You have successfully trained a machine learning model and deployed it. Next, you'll use your model to make predictions.
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