RapidCanvas Docs
  • Welcome
  • GETTING STARTED
    • Quick start guide
    • Introduction to RapidCanvas
    • RapidCanvas Concepts
    • Accessing the platform
  • BASIC
    • Projects
      • Projects Overview
        • Creating a project
        • Reviewing the Projects listing page
        • Duplicating a Project
        • Modifying the project settings
        • Deleting Project(s)
        • Configuring global variables at the project level
        • Working on a project
        • Generating the about content for the project
        • Generating AI snippets for each node on the Canvas
        • Marking & Unmarking a Project as Favorite
      • Canvas overview
        • Shortcut options on canvas
        • Queuing the Recipes
        • Bulk Deletion of Canvas Nodes
        • AI Guide
      • Recipes
        • AI-assisted recipe
        • Rapid model recipe
        • Template recipe
        • Code Recipe
        • RAG Recipes
      • Scheduler overview
        • Creating a scheduler
        • Running the scheduler manually
        • Managing schedulers in a project
        • Viewing the schedulers in a project
        • Viewing the run history of a specific scheduler
        • Publishing the updated data pipeline to selected jobs from canvas
        • Fetching the latest data pipeline to a specific scheduler
        • Comparing the canvas of the scheduler with current canvas of the project
      • Predictions
        • Manual Prediction
        • Prediction Scheduler
      • Segments and Scenarios
      • DataApps
        • Model DataApp
        • Project Canvas Datasets
        • Custom Uploaded Datasets
        • SQL Sources
        • Documents and PDFs
        • Prediction Service
        • Scheduler
        • Import DataApp
    • Connectors
      • Importing dataset(s) from the local system
      • Importing Text Files from the Local System
      • Connectors overview
      • Connect to external connectors
        • Importing data from Google Cloud Storage (GCS)
        • Importing data from Amazon S3
        • Importing data from Azure Blob
        • Importing data from Mongo DB
        • Importing data from Snowflake
        • Importing data from MySQL
        • Importing data from Amazon Redshift
        • Importing data from Fivetran connectors
    • Workspaces
      • User roles and permissions
    • Artifacts & Models
      • Adding Artifacts at the Project Level
      • Adding Models at the Project Level
      • Creating an artifact at the workspace level
      • Managing artifacts at the workspace level
      • Managing Models at the Workspace Level
      • Prediction services
    • Environments Overview
      • Creating an environment
      • Editing the environment details
      • Deleting an environment
      • Monitoring the resource utilization in an environment
  • ADVANCED
    • Starter Guide
      • Quick Start
    • Setup and Installation
      • Installing and setting up the SDK
    • Helper Functions
    • Notebook Guide
      • Introduction
      • Create a template
      • Code Snippets
      • DataApps
      • Prediction Service
      • How to
        • How to Authenticate
        • Create a new project
        • Create a Custom Environment
        • Add a dataset
        • Add a recipe to the dataset
        • Manage cloud connection
        • Code recipes
        • Display a template on the UI
        • Create Global Variables
        • Scheduler
        • Create new scenarios
        • Create Template
        • Use a template in a flow notebook
      • Reference Implementations
        • DataApps
        • Artifacts
        • Connectors
        • Feature Store
        • ML model
        • ML Pipeline
        • Multiple Files
      • Sample Projects
        • Model build and predict
    • Rapid Rag
  • Additional Reading
    • Release Notes
      • May 14, 2025
      • April 21, 2025
      • April 01, 2025
      • Mar 18, 2025
      • Feb 27, 2025
      • Jan 27, 2025
      • Dec 26, 2024
      • Nov 26, 2024
      • Oct 24, 2024
      • Sep 11, 2024
        • Aug 08, 2024
      • Aug 29, 2024
      • July 18, 2024
      • July 03, 2024
      • June 19, 2024
      • May 30, 2024
      • May 15, 2024
      • April 17, 2024
      • Mar 28, 2024
      • Mar 20, 2024
      • Feb 28, 2024
      • Feb 19, 2024
      • Jan 30, 2024
      • Jan 16, 2024
      • Dec 12, 2023
      • Nov 07, 2023
      • Oct 25, 2023
      • Oct 01, 2024
    • Glossary
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On this page
  • Building blocks and significance
  • Dag view options
  • Various options to create a machine learning flow on the canvas
  • Other Options Available on the Canvas
  • See also
  1. BASIC
  2. Projects

Canvas overview

PreviousMarking & Unmarking a Project as FavoriteNextShortcut options on canvas

Last updated 1 month ago

Canvas is a place where you can build data pipelines and machine learning models by importing datasets and adding recipes. Once you create a project, you are navigated to the Canvas, where you can create, test, and train models using various recipe types.

Types of Recipes

  • AI-Assisted Recipe – Enter a text prompt to generate a recipe in seconds using AI and integrate it into your pipeline.

  • Rapid Canvas Recipe – Automatically runs an end-to-end machine learning flow by simply uploading a dataset.

  • Code Recipe – Write custom code to transform data and generate outputs, including datasets, charts, text, or models. Designed specifically for data scientists.

  • Template Recipe – Provides predefined templates for common tasks such as data cleaning, data preparation, data analysis, feature engineering, model building, model prediction, and visualization.

The Canvas offers a flexible and efficient way to build and refine data pipelines.

You can perform the following tasks on the canvas to create flows:

  • Input the dataset or text file

  • Execute or run recipes (Template, AI-assisted, code, or rapid model)

  • Get the output dataset or a chart, text, or model

Getting familiar with these areas will help business users to build models more efficiently without writing any Python code or ML expertise. With a user-friendly interface, any user can get started with building machine learning models.

Building blocks and significance

The following are the building blocks you view while building the data pipeline.

Icon
Name
Description

Dataset

This icon represents a dataset. It is displayed when you import a dataset onto the canvas successfully, or when a dataset is generated after running a recipe.

Recipe

This icon represents a recipe. It executes various transforms related to data cleaning, preparation, analysis, feature engineering, model building, prediction and visualization.

Dashboard

This icon represents a dashboard. You can visualize the data presented in the form of charts and scatter plots.

Unbuilt recipe

This icon represents a recipe added to the flow but has no transformations added.

Running the recipe

This icon represents the recipe execution in progress.

Error

This icon represents a recipe error. It is displayed when the recipe is failed during its execution.

Model

This icon represents a model that is generated after running a recipe.

Artifact

This icon represents an artifact generated after running a recipe.

Empty dataset

This icon represents an empty output dataset. It is displayed when the recipe run is in progress.

Empty recipe

This icon represents a recipe added to the flow but is not executed.

Code recipe

This icon represents a code recipe that has been added to the data pipeline

Dag view options

Use these Dag view options to change the view of the data pipeline.

Icon
Name
Description

Zoom in

Use this option to enlarge the DAG.

Zoom out

Use this option to shrink the DAG.

Fit view

Use this option to fit the Dag into the size of the screen.

Auto arrange canvas nodes

Use this option to auto arrange canvas nodes. This formats the building blocks and lines in the flow to make it easier to read. It also ensures that the connectors do not overlap with another.

Curved connector

Use this option to use curved lines for aligning the building blocks.

Straight connector

Use this option to use straight lines for aligning the building blocks.

Save canvas nodes orientation

Use this option to save the order in which you arranged the building blocks on the canvas manually.

Expand all

Use this option to expand all the nodes in the data pipeline.

Collapse all

Use this option to collapse all the nodes in the data pipeline.

Snippet Generator

Use this option to generate AI snippet for each node on the canvas.

Various options to create a machine learning flow on the canvas

Use these options to build machine learning flows.

How to access?

Option
Description

Dataset

The option to upload a dataset onto the canvas to do the predictions.

Artifact

The option to add artifacts onto the canvas.

Model

The option to add an existing model to the canvas.

Template

The option to add pre-defined templates to the canvas to perform data cleaning, data pre-processing, feature engineering and model building.

AI-assisted

The option to use AI to generate the recipes or templates you want by providing a text prompt.

Rapid Model

The option to build machine learning models automatically by uploading the dataset and selecting the problem type.

Code

The option to write Python code to generate datasets and run recipes.

Text

The option to add text files (supported formats - txt, html, json or markdown)

Other Options Available on the Canvas

  • Run

    • Run: Executes the recipes using the data currently available in the datasets on the canvas. This option remains disabled until recipes are added to the canvas.

    • Run with Fresh Data: Retrieves fresh data from data connectors and runs the data pipeline.

  • RapidCanvas AI Guide – Generates the steps required to develop a model for the selected use case.

  • Switch Scenarios– Click the drop-down to switch between scenarios within the project.

  • Search Entities – Search for a specific entity in the canvas using the search option.

See also

To learn more about the other tabs on canvas, read the following sections:

Click on the plus icon on the canvas to see these options under Datasets and Recipes:

Canvas Overview
Projects
Scenarios
Schedulers
DataApps
Project Settings