---
title: "Create an Endpoint"
url: https://docs.catalyst.zoho.com/en/tutorials/billing-and-subscription-metrics/create-endpoint/
description: "Create a powerful ML pipeline that analyzed billing and subscription metrics for an OTT platform using the Catalyst QuickML components."
last_updated: 2026-09-29T06:01:01Z
service: All Services
type: tutorial
related_links:
  - name: "Pipeline Endpoints"
    url: /en/quickml/help/pipeline-endpoints
---

# Create Endpoint

Now create an endpoint for the above model to allow **[external applications](https://docs.catalyst.zoho.com/en/quickml/help/pipeline-endpoints/#model-tester)** to interact with the model seamlessly and get model responses.

1. Navigate to the *Endpoints* component in the left menu and click **Create Endpoint**.

![forecasting-tutorial-28.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/billing-subscription-metrics/forecasting-tutorial-28.webp)

2. Provide a name for the endpoint in the Endpoint Name field; (we'll name it **billing subscription model**), select endpoint type as **ML Model**, and select the model pipeline name from the drop-down values of the *Choose Model* field. Click **Create Endpoint**.

![forecasting-tutorial-29.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/billing-subscription-metrics/forecasting-tutorial-29.webp)

3. Once the endpoint is created, you can view its details and test the deployed model directly from the Test the model section. In this view (as shown in the screenshot), the Request panel displays the input format expected by the model—typically the most recent timestamp or feature values required for generating the next forecast.

For our use case, the model accepts the latest date value as input because the VAR model predicts the target variable based on the sequence of historical timestamps and related features processed during training.

When you click **Get Result**, the model processes this input and generates the forecasted value, which appears in the Response panel on the right. The output includes a predicted value associated with the next forecasted date. This helps users validate that the model is returning predictions in the correct structure and for the correct time step before integrating the endpoint into business applications.

![forecasting-tutorial-30.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/billing-subscription-metrics/forecasting-tutorial-30.webp)

4. Click **Publish** and use the endpoint URL to integrate the created ML model with any other applications.

![forecasting-tutorial-31.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/billing-subscription-metrics/forecasting-tutorial-31.webp)

5. Upon publishing the endpoint, it will generate API details—which include Endpoint API URL, scope, headers, and more—used to access the model from external applications.

![forecasting-tutorial-32.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/billing-subscription-metrics/forecasting-tutorial-32.webp)

> **Note:** You can also check out **[Endpoints Authentication](https://docs.catalyst.zoho.com/en/quickml/help/pipeline-endpoints/#external-oauth2-authentication)** document to implement pipeline authentication. This ensures secured access to endpoints, the ML models, and datasets.