treeverse/example-get-started

Demonstrates DVC's pipeline orchestration and experiment tracking through a complete ML workflow: data ingestion via `dvc add`/`dvc import`, multi-stage feature engineering and random forest training defined in `dvc.yaml`, and reproducible experiment management with `dvc exp run` for hyperparameter tuning. Uses Git tags to checkpoint incremental development steps, enabling learners to isolate and explore individual DVC commands while the preconfigured HTTP remote storage automatically manages artifact versioning and caching across pipeline stages.

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