removed images from folder

tutorials/cp-aibus-dar-swagger-ior-model/cp-aibus-dar-swagger-ior-model.md

@@ -8,11 +8,11 @@ author_name: Juliana Morais
 author_profile: https://github.com/Juliana-Morais
 ---
 
-# Use the Invoice Object Recommendation Business Blueprint to Train a Machine Learning Model
-<!-- description --> Train a machine learning model for the Data Attribute Recommendation service, using the Invoice Object Recommendation business blueprint.
+# Use the Invoice Object Recommendation (IOR) Business Blueprint to Train a Machine Learning Model
+<!-- description --> Train a machine learning model for the Data Attribute Recommendation service, using the Invoice Object Recommendation (IOR) business blueprint.
 
 ## You will learn
-  - How to train a machine learning model using the Invoice Object Recommendation business blueprint
+  - How to train a machine learning model using the Invoice Object Recommendation (IOR) business blueprint
   - How to deploy a machine learning model to get financial object predictions
 
 ---
@@ -22,15 +22,15 @@ author_profile: https://github.com/Juliana-Morais
 
 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`.
 
-<!-- border -->![Service Key](png-files/service-key-details.png)
+<!-- border -->![Service Key](service-key-details.png)
 
 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.
 
->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).
+>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).
 
 1. To be able to use the Swagger UI endpoints, you need to authorize yourself. In the top right corner, click **Authorize**.
 
-    <!-- border -->![Authorize](png-files/swagger-authorize.png)
+    <!-- border -->![Authorize](swagger-authorize.png)
 
 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.
 
@@ -40,32 +40,32 @@ For this tutorial, copy the URL of the Swagger UI for `mm` and open it in a brow
 
 3. Click **Authorize** and then click **Close**.
 
-    <!-- border -->![Authorize](png-files/swagger-token.png)
+    <!-- border -->![Authorize](swagger-token.png)
 
 
 
 ### Create a training job
 
 
-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.
+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.
 
-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).
+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).
 
-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.
+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.
 
 To create the training job, proceed as follows:
 
 1. Expand the endpoint **POST /jobs** by clicking on it. Then click **Try it out**.
 
-    <!-- border -->![Training Job Endpoint](png-files/job-endpoint.png)
+    <!-- border -->![Training Job Endpoint](job-endpoint.png)
 
-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.
+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.
 
-    <!-- border -->![Training Job Execute](png-files/job-execute.png)
+    <!-- border -->![Training Job Execute](job-execute.png)
 
 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.
 
-    <!-- border -->![Training Job Response](png-files/job-response.png)
+    <!-- border -->![Training Job Response](job-response.png)
 
 You have successfully created a training job.
 
@@ -78,15 +78,15 @@ To know when your training job has ended, you have to frequently check its statu
 
 1. Expand the endpoint `GET /jobs/{jobId}` by clicking on it. Then click **Try it out**.
 
-    <!-- border -->![Training Job Status Endpoint](png-files/job-status-endpoint.png)
+    <!-- border -->![Training Job Status Endpoint](job-status-endpoint.png)
 
 2. Fill the parameter `jobId` with `id` of your training job that you copied in the previous step. Click **Execute**.
 
-    <!-- border -->![Training Job Status Execute](png-files/job-status-execute.png)
+    <!-- border -->![Training Job Status Execute](job-status-execute.png)
 
 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.
 
-    <!-- border -->![Training Job Status Response](png-files/job-status-response.png)
+    <!-- border -->![Training Job Status Response](job-status-response.png)
 
 You have successfully trained a machine learning model.
 
@@ -99,15 +99,15 @@ To use the trained model, you need to deploy it. Once deployed, the model is rea
 
 1. Expand the endpoint `POST /deployments` by clicking on it. Then click **Try it out**.
 
-    <!-- border -->![Deployment Endpoint](png-files/deploy-endpoint.png)
+    <!-- border -->![Deployment Endpoint](deploy-endpoint.png)
 
 2. In the text area, replace the parameter `modelName` with the name of your model (`ior_tutorial_model`). Click **Execute** to deploy the model.
 
-    <!-- border -->![Deployment Execute](png-files/deploy-execute.png)
+    <!-- border -->![Deployment Execute](deploy-execute.png)
 
 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.
 
-    <!-- border -->![Deployment Response](png-files/deploy-response.png)
+    <!-- border -->![Deployment Response](deploy-response.png)
 
 
 
@@ -118,16 +118,14 @@ Finally, you have to ensure that your model is deployed successfully in order to
 
 1. Expand the endpoint `GET /deployments/{id}` by clicking on it. Then click **Try it out**.
 
-    <!-- border -->![Deployment Status Endpoint](png-files/deploy-status-endpoint.png)
+    <!-- border -->![Deployment Status Endpoint](deploy-status-endpoint.png)
 
 2. Fill the parameter `deploymentId` with the `id` of your deployment that you copied in the previous step. Click **Execute**.
 
-    <!-- border -->![Deployment Status Execute](png-files/deploy-status-execute.png)
+    <!-- border -->![Deployment Status Execute](deploy-status-execute.png)
 
 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.
 
-    <!-- border -->![Deployment Status Execute](png-files/deploy-status-response.png)
-
-You have successfully trained a machine learning model and deployed it. Next, you'll use your model to make predictions.
-
+    <!-- border -->![Deployment Status Execute](deploy-status-response.png)
 
+You have successfully trained a machine learning model and deployed it. Next, you'll use your model to make predictions.