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
  • RapidCanvas 101: A Quick Start Guide
  • 1. Getting Started
  • 2. Navigating the RapidCanvas Dashboard
  • 3. Connecting Your Data to RapidCanvas
  • 4. Workspaces and Projects
  • 5. Building an AI Solution in RapidCanvas
  • 6. Recipes in RapidCanvas
  • 7. Models in RapidCanvas
  • 8. AI Agents in RapidCanvas
  • 9. Schedulers in RapidCanvas
  • 10. Sharing Your AI Analysis
  • 11. Putting Your AI Solution to Work
  1. GETTING STARTED

Quick start guide

RapidCanvas 101: A Quick Start Guide

1. Getting Started

How to Sign In

  • Enrollment: Upon enrolling, you will receive an email with a sign-in portal link.

  • Bookmarking: Save the staging login page for quick access.


2. Navigating the RapidCanvas Dashboard

Key Features

  • User Profile and Settings:

    • Manage personal settings

    • Access support

  • Select Environment:

    • Dedicated workspace for AI solutions and data projects

  • Projects Overview:

    • Manage and review ongoing projects

  • Data Connections:

    • Integrate various data sources

  • Notebook:

    • Write and execute code interactively

  • DataApps:

    • Build custom dashboards for data visualization

  • Artifacts:

    • Store saved outputs or resources for future reuse


3. Connecting Your Data to RapidCanvas

Steps to Connect Data

  • Select a Data Source Type:

    • Options include databases, cloud storage, APIs, or flat files.

  • Enter Connection Details:

    • Provide the necessary credentials or upload files.

  • Validate and Map Data:

    • Use built-in tools to ensure data integrity and proper field mapping.

  • Test and Save:

    • Confirm that the data loads correctly and save the connection configuration.


4. Workspaces and Projects

What is a Workspace?

A Workspace is a dedicated space designed for an organization, team, or project, ensuring isolation to maintain data security and compliance.

Key Features of a Workspace

  • Custom Settings

  • Data Security

  • Access Control

How to Work in a Workspace

  • Access: Log in and enter your dedicated workspace.

  • Organize: Structure your data, projects, and resources.

  • Build: Start developing your AI solutions within a secured environment.

What is a Project?

A project is a structured workspace for managing all components of building an AI solution or performing an analysis.

Key Components of a Project

  • Data Integration

  • Model Development

  • AI Agents

  • Visualizations

  • Reports


5. Building an AI Solution in RapidCanvas

Step-by-Step Process

  1. Create a New Project:

    • Click the “+” button to start a new project.

    • Name your project and select the appropriate environment.

  2. Connect Your Data:

    • Integrate your data sources as described in the “Connecting Your Data” section.

  3. Prepare and Explore Data:

    • Use the AskAI tool to analyze, clean, and sort data.

  4. Choose Your AI Model:

    • Select from prebuilt models or create a custom model.

  5. Train the Model:

    • Run the model and refine it based on performance.

  6. Test and Refine:

    • Evaluate model accuracy and make necessary adjustments.

  7. Deploy and Automate:

    • Make your model accessible for use and automate workflows.

  8. Monitor and Optimize:

    • Track performance metrics and adjust as needed.


6. Recipes in RapidCanvas

What is a Recipe?

A recipe is a predefined sequence of steps and data configurations that standardizes the process of building an AI solution.

What a Recipe Does:

  • Automates Workflows

  • Ensures Consistency

  • Simplifies Complex Tasks

How to Build a Recipe

  1. Choose Recipe Type:

    • AI-Assisted Recipe: Uses a conversational AI guide.

    • Rapid Model Recipe: Auto-generated by RapidCanvas, but customizable.

    • Code Recipe: Manually connect to an API.

  2. Edit the Recipe:

    • Select and customize steps such as data cleaning, model selection, training, evaluation, and visualization.

  3. Save and Run:

    • Save your recipe for future use.

    • Run the recipe manually or schedule/automate its execution.

  4. Monitor Results:

    • Track outputs and performance.


7. Models in RapidCanvas

Different Types of Models and Their Use Cases

  • Binary Classification Model:

    • Purpose: Predicts outcomes in one of two categories.

    • Example: Predicting if a visitor will make a purchase.

    • Use Cases: Targeted marketing, fraud detection, automation.

  • Regression Model:

    • Purpose: Predicts continuous outcomes.

    • Example: Estimating how temperature affects lemonade sales.

    • Use Cases: Forecasting sales, planning, understanding relationships.

  • Multiclass Classification Model:

    • Purpose: Predicts outcomes with more than two categories.

    • Example: Recommending items in a clothing store.

    • Use Cases: Personalized recommendations, organizing data, enhancing customer experience.

  • Time Series Forecasting Model:

    • Purpose: Predicts future trends based on historical data.

    • Example: Forecasting ice cream sales based on seasonal patterns.

    • Use Cases: Inventory management, budget planning, scheduling.

  • Anomaly Detection Model:

    • Purpose: Identifies unusual patterns in data.

    • Example: Detecting a sudden surge in orders.

    • Use Cases: Fraud detection, performance monitoring, error identification.

  • Clustering Model:

    • Purpose: Groups similar data points together.

    • Example: Segmenting bookstore customers by preferences.

    • Use Cases: Targeted marketing, gaining customer insights, resource allocation.

Recipe vs. Model

  • Recipe:

    • Definition: A predefined collection of steps and data points.

    • Purpose: Standardizes workflows and embeds best practices.

  • Model:

    • Definition: The output of applying a recipe to data, which represents learned patterns.

    • Purpose: Analyzes new data and automates decision-making.


8. AI Agents in RapidCanvas

RapidCanvas AI Agents

  • Definition: Autonomous agents that automate machine learning processes.

  • Purpose: Simplify the integration and deployment of AI solutions.

  • Key Features:

    • Automation

    • Integration

    • Intelligence

    • User-friendliness

    • Versatility

  • Example Use Case: Dynamically adjusting product prices in e-commerce.

AskAI: The RapidCanvas AI Agent

  • Definition: An interactive chatbot within RapidCanvas.

  • Purpose: Provides insights and answers real-time queries.

  • How It Works: Processes natural language questions (e.g., “What was our best-selling product last month?”).


9. Schedulers in RapidCanvas

What is a scheduler ?

A scheduler is a defined task or process used to automate workflows and manage data.

Key Features

  • Automation

  • Task Execution

  • Scheduling

  • Integration

  • Monitoring and Reporting

  • Customization

Examples of Schedulers

  • Data Import Scheduler

  • Report Generation Scheduler

  • Alert Scheduler


10. Sharing Your AI Analysis

Methods to Share Outside RapidCanvas

  • Exporting Reports:

    • Formats: PDF, Excel/CSV.

  • Exporting Dashboards:

    • Formats: Image, PDF.

  • Exporting Data:

    • Formats: CSV, Excel.

  • Using APIs for Data Extraction:

    • Programmatically extract data.

  • Exporting AI Models or Solutions:

    • Formats: PMML, ONNX.

  • Sharing Visuals:

    • Capture screenshots or export high-quality images.

  • Collaborative Sharing:

    • Share via links or cloud storage.


11. Putting Your AI Solution to Work

Final Steps

  1. Finalize Your Solution:

    • Review, test, and document the AI model.

  2. Deploy the Model:

    • Options: On-premises or cloud deployment.

  3. Integration:

    • Ensure API integration and connect to real-time data sources.

  4. Automate Tasks:

    • Set up scheduled data processing tasks.

  5. Monitor Performance:

    • Track key metrics and configure alerts.

  6. User Training and Support:

    • Educate users and provide necessary help resources.

  7. Continuous Improvement:

    • Gather feedback and update the model regularly.

  8. Reporting and Analysis:

    • Summarize outcomes and use insights to inform decisions.

PreviousWelcomeNextIntroduction to RapidCanvas

Last updated 2 months ago

Page cover image