Chapter 4. Deploying and testing a model

Procedure

1. In your JupyterLab environment, open the 2_save_model.ipynb file.
2. Follow the instructions in the notebook to make the model accessible in storage.

Procedure

1. In the OpenShift AI dashboard, navigate to the project details page and click the Deployments tab.

2. Click Deploy model.

   The Deploy a model wizard opens.

3. In the Model details section, provide information about the model:

   1. For Model location, select Existing connection and then select My Storage.

   2. Enter the following values from your MinIO storage connection:

      • Access Key
      • Secret Key
      • Endpoint
      • Region
      • Bucket
   3. For Path, enter models/fraud.
   4. For Model type, select Predictive model.
   5. Click Next.

4. In the Model deployment section, configure the deployment:

   1. For Model deployment name, enter fraud.
   2. For Description, enter a description of your deployment.
   3. For the hardware profile, keep the default value.
   4. For Model framework (name - version), select onnx-1.
   5. For the Serving runtime field, accept the auto-selected runtime, OpenVINO Model Server.
   6. Click Next.
5. In the Advanced settings section, accept the defaults by clicking Next.
6. In the Review section, click Deploy model.

Procedure

1. In the OpenShift AI dashboard, navigate to the project details page and click the Deployments tab.

2. Take note of the model’s Inference endpoint URL. You need this information when you test the model API.

   If the Inference endpoint field has an Internal endpoint details link, click the link to open a text box that shows the URL details, and then take note of the restUrl value.

   

   NOTE: When you test the model API from inside a workbench, you must edit the endpoint to specify 8888 for the port. For example:

   http://fraud-predictor.fraud-detection.svc.cluster.local:8888

3. Return to the JupyterLab environment and try out your new endpoint.

   Follow the directions in 3_rest_requests.ipynb to try a REST API call.