data-version-control

Dataset versioning tracks large file states with DVC so you can retrieve exact inputs later. It enables Git-like snapshot capabilities for data-intensive ML research without storing heavy files directly in Git.

Pipeline automation orchestrates ML stages by defining preprocess, train, and evaluate steps in a dvc.yaml file. DVC automatically detects changed dependencies and executes only the modified pipeline stages during reproduction.

Experiment tracking compares metrics, artifacts, and hyperparameter settings across multiple runs using DVC experiments. It allows researchers to evaluate different preprocessing settings and identify the best-performing model configuration.

Remote cache configuration is required to share reproducible data workflows with collaborators and CI systems. DVC remotes store versioned dataset snapshots and pipeline outputs, enabling exact environment reproduction across machines.

Reproducing a pipeline execution requires running the dvc repro command in a configured DVC repository. This single command executes the full workflow defined in dvc.yaml, regenerating exact dataset states and model metrics.