Publishing Models

Publishing Models

You can publish algorithms as models after their successful execution. A model can be reused in a workbook for training and experimenting or can be used in a workflow for production.

 Notes:
  • This functionality is available only for Machine Learning Regression, Machine Learning Classification, AutoML, and Textual Classification algorithms.
  • You can publish an algorithm only after it has run successfully.


To publish a model, follow the steps given below.

  1. Open the workbook that you want to publish as a model. Refer to Opening a Workbook.
    1. Run the node that you want to publish. Refer to Running a Workbook.

  2. To publish the model, click the algorithm, click the vertical ellipsis, and then click Publish as a Model icon ().Your model is published. The published models are available in model studio and data integrator under Models. You can use them in your workbooks and workflows.

    • Related Articles

    • Publishing a Dasboard

      You can publish a Dashboard after it is created. Publishing a Dashboard generates a public link for the Dashboard, which can then be shared with the business users. Publishing a Dashboard facilitates business users to Receive the URL and open the ...

    • Building Algorithm Flow in a Workflow Canvas

      Building algorithm flow in a Workflow Canvas is similar to building algorithm flow in a Workbook Canvas. You can use your trained algorithms here. To build algorithm flow in a Workflow Canvas, refer to Building Algorithm Flow in a Workbook Canvas. ...

    • Model Validation

      Model validation is an enhancement of publishing a model. You can use this feature to explore the result of the published model for a selected dataset. In model validation, you can use the published model for the selected algorithm with the same ...

    • MLOPS - Machine Learning Operations

      Introduction: Why Rubiscape MLOps? Rubiscape MLOps provides an end-to-end environment for building, tracking, publishing, and serving machine learning models. It ensures experiment reproducibility, streamlined deployment, and centralized model ...

    • Building Algorithm Flow in a Workbook Canvas

      The workbook canvas is your experimental area. You can create, run, and train your algorithm flow in workbook canvas using a simple drag-and-drop method, to add datasets and algorithms. The table given below provides major differences between the ...