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
Powered by GitBook
On this page
  • Table of Contents
  • Basic
  • User
  • Project
  • Canvas
  • Connectors
  • Data table
  • Recipe
  • Transform
  • Advanced
  • Workspace
  • Environment
  • Template
  • Artifact
  • Scenarios
  • Segments
  1. GETTING STARTED

RapidCanvas Concepts

PreviousIntroduction to RapidCanvasNextAccessing the platform

Last updated 2 months ago

Table of Contents

Basic

User

Click to expand

Anyone with access to the web or notebook interface of RapidCanvas is a user. There are two types of users: Admin and Read-Only Users. Admin users have the privilege to invite other team members to the tenant. All users get access to RC by invitation to join a tenant. All users can create and collaborate on projects in tenants to which they have access. A user can be part of multiple tenants with different roles.

Project

Click to expand

A project on RapidCanvas is typically an exercise to solve a business-relevant data science problem. A project is a combination of user and/or organizational assets such as data, recipes, and dashboards. It encapsulates the journey from raw data to predicting whatever the business requires. A project output can be a productionized Machine Learning model which predicts on new incoming data, or it can be a dashboard which shows relevant data metrics for your team.

Use Case - Your marketing team would like to solve 2 problems: Recommend new products to users based on their past purchases and customize coupon recommendations for each user. Both of these can be tackled by creating 2 separate projects.

Projects are created in tenants and can be associated with custom environments.

Canvas

Click to expand

Canvas in RapidCanvas is a dynamic acyclic graph (also called DAG) which allows business users to build a sequence of data input, data processing, and modeling steps to build a project. The canvas provides a holistic view of the project and simplifies explainability.

A canvas consists of nodes and connectors. Nodes can be either data sets, recipes, or dashboards. More about them in the next few sections.

Connectors

Click to expand

Connectors are locations typically where your data resides. It can either be one of local storage (your computer), cloud storage (Amazon S3, GCP, Azure Blob Storage, MongoDB, MySQL, Amazon Redshift, and Snowflake), or on-premise. RapidCanvas provides the ability to connect to these connectors or data sources and fetch data into your RapidCanvas interface.

Data table

Click to expand

Data imported into RapidCanvas or output data generated within an RapidCanvas project after processing is considered a data table. Data tables are bound by a project and are represented by a rectangle on the canvas.

Recipe

Click to expand

A recipe is either a single transform or a collection of multiple transforms, which provides the ability to process data tables, build, and run machine learning models in RC. A recipe can output a data table, dashboard, or a model. A recipe is represented by a circle on the canvas.

Transform

Click to expand

A transform is a data processing unit which can take individual or multiple inputs and generate an output. A simple example of a transform can be concatenating 2 columns. Inputs can be first name and last name, and the output can be the full name.

Advanced

Workspace

Click to expand

A workspace is required for a group of users to work. Data and projects inside a workspace are only accessible to the users who are part of the workspace. Typically, organizations with a large number of projects have multiple workspaces to organize streams of AI/ML Problems they solve.

Environment

Click to expand

An environment is an infrastructure unit created under a tenant. It defines the compute resources and user-selected packages that you may need to run your projects. RapidCanvas provides a set of standard configurations of compute resources. In a tenant, users can create multiple custom environments. In a tenant, each project can be associated with a custom environment.

Standard configurations of compute resources provided by RapidCanvas are:

  • SMALL: 1 Core, 2GB Memory

  • MEDIUM: 2 Cores, 4GB Memory

  • LARGE: 4 Cores, 8GB Memory

  • CPU_LARGE: 8 Cores, 16GB Memory

  • MAX_LARGE: 12 Cores, 32GB Memory

  • EXTRA_MAX_LARGE: 12 Cores, 48GB Memory

Template

Click to expand

A template is a building block of the transform. A template defines the logic of the data transformation, the inputs that need to be passed, and the output that will be generated.

RapidCanvas provides a library of system templates and is continuously adding more to the library. In case the system templates are not adequate, notebook users will be able to create templates as per their project needs. Business users on the web interface of RapidCanvas would see only Recipes and Transforms and would be limited to the use of system templates and any custom templates developed and made available inside a tenant by their team of notebook users.

Artifact

Click to expand

Any project-related asset such as models that are built by users can be saved as artifacts. These artifacts can be used across projects. Typically, generic and reusable assets are saved as artifacts and are used in multiple projects. Creating, saving, and accessing artifacts is available only to notebook users currently.

Scenarios

Click to expand

A scenario is created within a project and allows you to run a project only when certain conditions are met. Scenarios can be used to run your project with different datasets and changing parameters which go into a recipe.

Segments

Click to expand

Within a project, Segments are subsets of your input data sets that meet user-defined conditions. Segments can be used to slice and dice your data and associate with scenarios. This allows you to run your scenarios with only some of the data.

Use Case - You have 12 months of historic data; however, you would like to build multiple models each with varying amounts of historic data. You can create segments with 1 month, 3 months, 6 months, 12 months of data, etc., and create corresponding scenarios to build different models and compare their performance.

Basic
Advanced
Page cover image