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Kumo Foundation Models

Which foundation model should I choose?

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Choose by your data structure and prediction task:

  • Kumo Tabular: Classification or regression on a single table of numerical and categorical features. Text, images, and timestamps need preprocessing into supported features; related tables and relationships are not direct inputs.
  • Kumo Relational: Classification or regression using information across related tables. Check the model version and runtime documentation for supported tasks and outputs.
  • Kumo Forecast or Kumo Anomaly: Time-series forecasting or anomaly detection, respectively.

Tabular and Relational need labeled examples as context even though they do not require task-specific training. Prediction quality depends on how well that context represents the rows you want to score. For Tabular, accuracy can also degrade on tables far outside the pretraining ranges.

See Choose a Model for model cards, required inputs, and execution options. Before deployment, evaluate the model on your own data. For memory requirements, see Context-Size Limits.