Data Science Lab
  • What is Data Science Lab?
  • Accessing the Data Science Lab Module
  • Homepage
  • List Projects
  • List Feature Stores
  • Create
    • Create Project
      • Container Status Message
    • Create Feature Store
  • Registered Models and APIs
  • Settings
  • Trash
  • Tabs for a DSL Project
    • Workspace
      • Workspace Folders
        • Repo Folder Attributes
          • Notebook Actions
            • Export
            • Register as Job
            • Notebook Version Control
            • Share
            • Delete
            • Information
        • Repo Folder Attributes for a Repo Sync Project
          • File Attributives
        • Utils Folder Attributes
          • Utility Actions
        • Files Attributes
      • Working with the Workspace tab
        • Create
        • Import
          • Importing Notebook
          • Pull from Git
        • Adding File and Folders
      • Linter
      • Git Console
    • Data
      • Adding Data
      • Data List Page
    • Model
      • Import Model
      • Explainer Generator
      • Export to GIT/ Model Migration
      • Model Explainer
      • Share a Model
      • Register a Model
      • Unregister a Model
      • Register a Model as an API Service
        • Register a Model as an API
        • Register an API Client
        • Pass Model Values in Postman
      • Delete Model
    • AutoML
      • Creating AutoML Experiment
      • AutoML List Page
        • View Explanation
          • Model Summary
          • Model Interpretation
            • Classification Model Explainer
            • Regression Model Explainer
            • Forecasting Model Explainer
          • Dataset Explainer
  • Data Science Notebook
    • Preview File
    • Save as Notebook
    • .ipynb File Cells
      • Using a Code Cell
      • Using a Markdown Cell
      • Using an Assist Cell
    • Resource Utilization Graph
    • Taskbar
    • Actions Icons
  • Model Creation using Data Science Notebook
  • Notebook Operations
    • Data
      • Copy Path Functionality
    • Secrets
    • Algorithms
    • Transforms
    • Artifacts
    • Variable Explorer
    • Writers
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Notebook Operations

This section aims at describing the various operations for a Data Science Notebook.

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Last updated 10 months ago

Please Note: The Notebook Operations may differ based on the selection of the project environments.

A Data Science Notebook created under the PyTorch or TensorFlow environment will contain the following operations:

  • Data: Add data and get a list of all the added datasets.

  • ​Secrets: You can generate Environment Variables to save your confidential information from getting exposed.

  • ​Algorithms: You can get steps to do Algorithm Settings and Project-level access to use Algorithms inside Notebook.

  • ​Transforms: Save and load models with transform script, register them, or publish them as an API through the DS Lab module.

  • ​Models: You can train, save, and load the models (Sklearn, Keras/TensorFlow, PyTorch). You can also register a model using this tab. Refer to for more details.

  • Artifacts: You can save the plots and datasets as Artifacts inside a DS Notebook.

  • ​Variable Explorer: Get detailed information on Variables declared inside a Notebook.

  • Writers: Write the DSL experiments' output into the database writers' supported range.

Model Creation using Data Science Notebook