---

name: data-pipeline-utilities description: "Data generation, preprocessing, metrics, model selection, feature extraction, explainers, and time-series utilities for cuML workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

cuML data-pipeline utilities

Use this sub-skill when a cuML workflow needs supporting utilities rather than a full model-training recipe: synthetic data, train/test or K-fold splits, preprocessing, categorical/text feature preparation, metrics, pairwise distances/kernels, model explainers, or time-series data/schema utilities.

Route here for

• cuml.datasets: make_blobs, make_classification, make_regression, make_arima.
• cuml.model_selection: train_test_split, KFold.
• cuml.preprocessing: scalers, normalizers, imputers, encoders, binarizers, TargetEncoder, and functional helpers.
• cuml.metrics: classification, regression, clustering, pairwise distance, and pairwise kernel metrics.
• cuml.feature_extraction.text: CountVectorizer, HashingVectorizer, TfidfVectorizer.
• cuml.explainer: KernelExplainer, PermutationExplainer, TreeExplainer route and setup notes.
• cuml.tsa and make_arima only for utility/schema guidance; these APIs are deprecated and should be handled carefully.

Route away

• Full estimator selection, fitting, prediction, serialization, and output-type workflows: use sibling sub-skill python-estimators.
• Distributed or multi-GPU Dask variants of preprocessing/text/model workflows: use sibling sub-skill distributed-dask.
• Zero-code-change scikit-learn acceleration via cuml.accel: use sibling sub-skill sklearn-accel.
• Source builds, C++ examples, native CI test selection, or CUDA toolchain diagnosis: use sibling sub-skill native-build-and-cpp or root troubleshooting.

Operating procedure

1. Read references/api-reference.md to identify the exact utility surface, signatures, input containers, output conventions, and deprecations.

2. Read references/workflows.md for compact recipes that combine generation, splitting, preprocessing, target/text encoding, metrics, explainers, and time-series schemas.

3. Read references/troubleshooting.md when imports, CUDA, container types, category handling, metrics, text vectorization, SHAP, or time-series schemas fail.

4. For a fast local check, run the bundled smoke script from the generated skill tree:

   python sub-skills/data-pipeline-utilities/scripts/data_utility_smoke.py --help
   python sub-skills/data-pipeline-utilities/scripts/data_utility_smoke.py --case core
   

Keep utility runs tiny unless the downstream task explicitly asks for scale or benchmark evidence. Do not infer estimator quality from utility-only metrics; hand off full training or model debugging to the estimator sub-skill.