Canvas overview
Last updated
Last updated
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.
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.
The following are the building blocks you view while building the data pipeline.
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
Use these Dag view options to change the view of the data pipeline.
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.
Use these options to build machine learning flows.
How to access?
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)
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.
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:
