---
title: "Create an ML Pipeline"
url: https://docs.catalyst.zoho.com/en/tutorials/cancer-detection/create-ml-pipeline/
description: "Create and configure a powerful ML pipeline that can be used to predict the chances of being affected by breast cancer using the Catalyst QuickML service."
last_updated: 2026-10-30T17:47:18Z
service: All Services
type: tutorial
related_links:
  - name: "ML Algorithms in QuickML"
    url: /en/quickml/help/ml-algorithms/classification-algorithms
  - name: "Operations in QuickML"
    url: /en/quickml/help/operations-in-quickml/encoding
---

# Create ML Pipeline

In this section, we will be building a prediction ML model using the preprocessed datasets in the previous section. The datasets will be the input to the **[ML Pipeline Builder](https://docs.catalyst.zoho.com/en/quickml/help/pipeline-builder-interface/walkthrough/)** which enables you to define the model's architecture and select a target column for prediction.

**To create a ML pipeline:**

1. Navigate to the *Pipelines* component in the left menu and click **Create Pipeline**.

   ![cancer-detection-20.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/cancer-detection/cancer-detection-20.webp)

2. In the *Create Pipeline* pop-up, choose the desired Pipeline Type. Here, select **Prediction** as your pipeline type.

   ![cancer-detection-21.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/cancer-detection/cancer-detection-21.webp)

3. Enter **Pipeline_B** as the pipeline name, Choose Dataset as **Cancer_detection_A**, and Choose Target Column should be **diagnosis**. Note, the model name will be auto-populated based on the pipeline name. 

4. Choose the **Retrain model** when the dataset is updated checkbox to retrain the pipeline whenever the dataset is updated with new data. 
  
   **Note:** checkout this document on **[periodic-sync](https://docs.catalyst.zoho.com/en/quickml/help/periodic-sync/)**, and to create an auto-generated pipeline using AutoML refer to the document on **[AutoML pipeline](https://docs.catalyst.zoho.com/en/quickml/help/periodic-sync/)**.

   ![cancer-detection-22.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/cancer-detection/cancer-detection-22.webp)

5. Click **Create**. The pipeline details page will be displayed.

   ![cancer-detection-23.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/cancer-detection/cancer-detection-23.webp)

Next, configure the pipeline by defining the nodes in the ML Pipeline Builder interface.

## Data Type Conversion

Because the target column "**diagnosis**" contains categorical data of type **String**, we will encode it for further ML training standards.

1. In the **Operations** menu, navigate to *ML operations->Encoding->Label Encoder*. 

2. Drag and drop the **[Label Encoder node](https://docs.catalyst.zoho.com/en/quickml/help/operations-in-quickml/encoding/#label-encoding)** to the ML Pipeline Builder Interface. Connect the Label Encoder node to the Source node. 

   ![cancer-detection-24.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/cancer-detection/cancer-detection-24.webp)

This operation will convert the column values of type String to Integer while maintaining the order and preserving data accuracy.

## Hyperparameter Tuning

For any ML model, it's mandatory to implement an ML algorithm based on which the model will be trained. In this tutorial, we will be implementing the **[logistic classification algorithm](https://docs.catalyst.zoho.com/en/quickml/help/ml-algorithms/classification-algorithms/#logistic-regression)** to configure the tuning parameters for the ML model to ensure it is optimized for our preprocessed dataset.

1. In the *Operations* menu, navigate to *ML operations->Algorithm->Classification->Logistic Regression*. 

2. Drag and drop the **Logistic Regression** node in the Pipeline Builder. The node will be connected to the **Destination** node automatically. 

3. Connect the **Label Encoder** and the **Logistic Regression** nodes. For the Logistic Regression node, we will go with the default configuration. Click **Save**.

   ![cancer-detection-25.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/cancer-detection/cancer-detection-25.webp)

Proceed with executing the pipeline by clicking on **Execute** for further evaluation and deployment.

![cancer-detection-26.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/cancer-detection/cancer-detection-26.webp)

Click **Execution Stats** to view more details about each stage of the execution in detail.

![cancer-detection-27.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/cancer-detection/cancer-detection-27.webp)

Upon successful execution of the ML Pipeline, the prediction model is created and will be displayed under the Models section.

You can view the details of the model on the *Models details* page by clicking on the model name.

![cancer-detection-28.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/cancer-detection/cancer-detection-28.webp)

Additionally, the accuracy of the generated model can be evaluated and viewed in the Metrics section of the *Models details* page. This provides valuable insights on the performance and effectiveness of the model in making predictions on the data.

![cancer-detection-29.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/cancer-detection/cancer-detection-29.webp)