Skip to main content
Version: 4.5

Create your First Endpoint

To Create the First Enpoint you need to Deploy the Model first.

How to Deploy the Model from Katonic Platform​

To deploy the model, the user needs to go to the deployment section of the platform and follow the below steps:

  • log-in to Katonic Platform and navigate to Deployment

  • Click on Model Deployment

  • Provide name of the deployment

  • Select Custom Model as deployment type

  • Provide the GitHub token

  • Your username will appear once the token is passed

  • Select the account type

  • Select the branch name

  • Select Python version

  • Select model type

  • Select pods range

  • Select resources

  • Click on Deploy.

  • Once your Custom Model API is created you will be able to view it in the Deploy section where it will be in "Processing" state in the beginning. Click on refresh to update the status. Keep checking the logs, to see the current status of deployment.

  • You can also check out the logs to see the progress of the deployment using Logs option.

  • Once your Model API is in the Running state you can check consumption of the hardware resources from Usage option.

  • You can access the API endpoints by clicking on API.

There are two APIs under API URLs:

Model Prediction API endpoint: This API is for generating the prediction from the deployed model Here is the code snippet to use the predict API:

MODEL_API_ENDPOINT= "Prediction API URL"
SECURE_TOKEN="Token"
data = {"data":"Define the value format as per the schema file"}
result = requests.post(f"{MODEL_API_ENDPOINT}", json = data,verify=False, headers = {"Authorization":SECURE_TOKEN})
print(result.text)

Model Feedback API endpoint: This API is for monitoring the model performance once you have the true labels available for the data. Here is the code snippet to use the feedabck API. The predicted labels can be saved at the destination sources and once the true labels are available those can be passed to the feedback URL to monitor the model continuously.

   MODEL_FEEDBACK_ENDPOINT= "Feedback API URL"
   SECURE_TOKEN="Token"
   true = "Pass the list of true labels"
   pred = "Pass the list of predicted labels"
   data = {"true_label":true,"predicted_label":pred}
   result = requests.post(f"{MODEL_API_ENDPOINT}", json = data,verify=False, headers = {"Authorization":SECURE_TOKEN})
   print(result.text)
  • Click on the Create API token to generate a new token in order to access the API
    • Give a name to the token.
    • Select the Expiration Time
    • Set the Token Expiry Date
    • Click on Create Token and generate your API Token from the pop-up dialog box.

Note: A maximum of 10 tokens can be generated for a model.Copy the API Token that was created. As it is only available once, be sure to save it.

  • Under the Existing API token section you can manage the generated token and can delete the no longer needed tokens.
  • API usage docs briefs you on how to use the APIs and even gives the flexibility to conduct API testing.
    • To know more about the usage of generated API you can follow the below steps
    • This is a guide on how to use the endpoint API. Here you can test the API with different inputs to check the working model. In order to test API you first need to Authorize yourself by adding the token as shown below. Click on Authorize and close the pop-up.
    • Once it is authorise you can click on Predict_Endpoint bar and scroll down to Try it out.
    • If you click on the Try it out button, the Request body panel will be available for editing. Put some input values for testing and the number of values/features in a record must be equal to the features you used while training the model.
    • If you click on execute, you would be able to see the prediction results at the end. If there are any errors you can go back to the model card and check the error logs for further investigation.
  • To delete the unused models use the Delete button.
  • You can also modify the resources,version minimum and maximum pods of your deployed model by clicking the Edit option and saving the updated configuration.
  • Click on Monitoring, and a dashboard would open up in a new tab. This will help to monitor the effectiveness and efficiency of your deployed model. Refer the Model Monitoring section in the Documentation to know more about the metrics that are been monitored.