omega-ml
  • Introduction
  • User Guide
    • Getting started
    • Generative AI
    • Classic ML
      • Tutorial for classic ML
        • Loading datasets for training
        • Training models
        • Evaluating models
        • Deploying models
        • Adding business logic to models
        • Serving models for real-time predictions
        • Tracking and monitoring models
      • Working with models
      • Working with datasets
        • Storing and retrieving data
        • Filtering Data
        • Large, Out of Core-sized DataFrames
    • Task Automation
    • Deployment and Operations
    • Command-line interface
    • Advanced features
  • Deploying omega-ml
  • Extending omega-ml
  • Reference
  • Support
  • Changes
omega-ml
  • User Guide
  • Classic ML
  • Tutorial for classic ML
  • Working with datasets
  • View page source

Working with datasetsΒΆ

  • Storing and retrieving data
    • Native Python objects
    • Pandas DataFrames, Series
    • External Sources
    • Accessing DBMS via SQL
    • Storing and retrieving files
  • Filtering Data
    • Query filtering
  • Large, Out of Core-sized DataFrames
    • Working with MDataFrame
    • MDataFrame Operations
    • Aggregation Framework
    • Debugging
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