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
  • New features
  • Enhancements
  • Bug fixes
  1. Additional Reading
  2. Release Notes

Oct 25, 2023

New features

This release includes the following features:

AI Guide on Project Canvas

  • Introducing an AI guide that offers users insights on machine learning project implementation based on their specific use case and industry.

  • Engage in a dynamic conversation by asking follow-up questions.

Rapid Model Recipe

  • Presenting a simplified way to generate models. Upload a flat file and let the Rapid Model Recipe take care of the rest.

  • Current support includes Binary Classification, Regression, and Multi-Class Classification.

  • Stay tuned for upcoming support for Time Series Forecasting, Clustering, and Anomaly Detection in our next release.

Enhancements

Data Import

  • Seamless fetching of table-form data from MongoDB and Snowflake sources. Import this data directly onto the canvas for transformative operations.

  • Integration with Fivetran connectors now permits data imports directly into projects.

Other Enhancements

  • Interface adjustments on RapidCanvas for Windows OS users

  • Enhanced data consistency and accuracy when handling files with special characters or separators.

  • Introduced job count linked to a project under the Environments tab for more transparency.

Bug fixes

These are the issues resolved as part of this release:

UI and Design

  • Long filenames no longer overflow in the "Create New Dataset" window.

  • Various alignment and UI improvements across Recipe, Tenant, and Environment pages for a streamlined experience.

Data Handling

  • Resolved encoding/separation errors for uploaded files, offering on-the-spot corrections.

  • Addressed inconsistencies with data type and ontology displays.

Functionality

  • Improved feedback mechanisms for data uploads, ensuring users receive accurate success/failure messages.

  • Better validation across the environment page for user inputs.

  • User alerts have been optimized for various platform actions, such as environment relaunches.

  • Enhanced error reporting for recipes, ensuring the UI reflects backend statuses accurately.

Environment and Resources

  • Improved tooltips, feedback, and options related to environment status, launch, and usage.

  • Addressed environment naming and package editing restrictions for better platform consistency.

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Last updated 3 months ago