Canvas overview

Canvas is a workspace where you can create flows or machine learning models by importing datasets and adding recipes. After you create a project, you will be navigated to the Canvas where you can create, test, and train models using various recipe types. There are predefined templates available for data cleaning, data preparation, data analysis, feature engineering, model building, model prediction, and visualization. Specifically, the AI-assisted recipe enables you to provide the text prompt to generate the code recipe in seconds and use it in the pipeline. You can use a rapid canvas recipe to run end-to-end ML flow automatically by uploading a dataset.

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

Input the dataset
Execute or run recipes (Template, AI-assisted or rapid model)
Get the output dataset or a chart, 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 during the flow creation.

Building blocks used in the flow
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.

Dag view options

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

Options on Dag
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 ensure 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.

Various options to create a machine learning flow on the canvas

Use these options to build machine learning flows.

How to access?

Click on the plus icon ico1 on the canvas to see these options under Datasets and Recipes:

Options on Dag
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 preexisting 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.

The other options you can view on the canvas are:

Run option to run the recipes in the canvas. This option will remain disabled until the recipes are added to the canvas.

RapidCanvas AI guide to generate the steps required to develop a model for the selected use case.

Click the drop-down to switch between scenarios within the project.

Search for a specific entity in the canvas, using the Search entities option.

AI Guide

Use the AI guide option to learn what is needed to develop a model and make predictions on the dataset, be it predicting the car prices, employees who have high chances of getting promoted, fraudulent transactions and so on.

Click on the project in which you can to use the AI guide. It takes you to the canvas page.
Click the chat icon on the bottom of the page. This opens the RapidCanvas AI Guide chat widget.

Select the vertical into which the dataset on which you want to make predictions fall into. Here, we have selected the vertical as Manufacturing and Industry 4.0.

Provide the use case you want to solve using machine learning. The use case provided is to predict car prices.
Click Submit. The AI will generate the output with detailed view of different data transformations you must perform to predict the car prices on the dataset.