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Kumo TabularUser GuideExamples and Notebooks

Notebooks

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Download this quickstart notebook to learn how Kumo Tabular uses labeled context rows to predict outcomes for new rows without training a task-specific model. The example classifies breast masses as benign or malignant using four features from the Mammographic Mass dataset on Kaggle.

In the notebook, you will:

  • Install and configure structured-data-models-client.
  • Connect to a deployed Kumo Tabular NVIDIA NIM inference service.
  • Read the sample CSV from a Snowflake stage and prepare labeled context and test rows.
  • Build and inspect a schema, then submit a classification request.
  • Read class probabilities and compare accuracy and ROC AUC with a majority-class baseline.
  • Run the same task using Predictive Query Language (PQL).

Before you begin, you need a deployed Kumo Tabular NIM endpoint and an active Snowpark session. Download the Mammographic Mass dataset from Kaggle, get the Cleaned_data.csv file, and upload it to a Snowflake stage. Use a notebook environment with pandas, NumPy, scikit-learn, and requests available. The model runs on the NIM server, so the notebook machine does not need a GPU.

For client installation and connection requirements, see Install and Connect.