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DVC (Data Version Control)

Data Version Control (DVC) (Data Version Control) is an open-source data and model versioning, experiment tracking, and pipeline management tool for Machine Learning (ML) workflows (ML operations / Data Lifecycle Management (DLM)).

  • Versioning of datasets, ML models, and experiments using Git-compatible workflows (data and model version control).
  • Definition and execution of data processing and ML training pipelines as reproducible DAGs (workflow orchestration / pipeline management).
  • Experiment tracking with metrics, parameters, and artifacts, including comparison and navigation across experiment runs (ML experiment management).
  • Remote storage integration for large files and artifacts across object stores, network storage, and local backends (artifact and data storage integration).
  • Collaboration support for ML teams through shared pipelines, data registries, and experiment history (collaborative ML operations).

More About DVC

DVC (Data Version Control) addresses data and model management in ML projects by extending Git-based workflows to large datasets, ML models, and experiment artifacts (ML operations / DLM). It targets teams that need reproducibility, traceability, and collaboration across code, data, and training runs. DVC integrates with Git repositories but keeps large binary assets in external storage, enabling reproducible ML projects without storing large files directly in Git.

At its core, DVC provides mechanisms to version datasets and models using lightweight metafiles tracked in Git while the actual data resides in pluggable remote backends (data and model version control). Supported remotes include local file systems, network file shares, and cloud object storage such as S3-compatible systems, Ground Control Segment (GCS), Azure Binary Large Object (BLOB), and other standard object-store protocols (artifact and data storage integration). This separation allows teams to manage commits and branches that correspond to specific data and model snapshots while avoiding Git repository bloat.

DVC also defines and executes ML pipelines as directed acyclic graphs described in configuration files (workflow orchestration / pipeline management). Each pipeline stage declares inputs, outputs, and commands, enabling reproducible data processing and model training. DVC tracks dependencies between stages and supports incremental execution, so only stages affected by changes are recomputed. This behavior aligns with enterprise build and Continuous Integration and Continuous Deployment (CI/CD) patterns, enabling automated retraining and redeployment workflows when code or data changes.

Experiment tracking is another DVC capability, enabling teams to manage metrics, hyperparameters, and artifacts for multiple runs (ML experiment management). DVC can record experiment parameters and outputs, compare experiments, and maintain an auditable history of changes. This supports model selection, governance, and auditability in regulated or controlled environments. Experiments can be organized using branches, tags, and experiment-specific references inside the same repository, aligning with existing Git practices.

In enterprise environments, DVC is typically used alongside CI/CD tools, orchestrators, and Machine Learning Operations (MLOps) platforms, where it acts as the versioning and pipeline layer for ML assets (MLOps / ML tooling integration). Teams use DVC to standardize how data scientists and engineers share datasets, update models, and reproduce experiments across environments such as local workstations, on-premises (on-prem) clusters, and cloud infrastructure. DVC’s reliance on text-based configuration and Git-compatible workflows aligns with Infrastructure-as-Code (IaC) and policy enforcement practices.

From a taxonomy perspective, DVC fits into categories including data and model version control, ML experiment tracking, and pipeline orchestration for ML. It interacts with storage systems through standard object storage and file system interfaces and integrates into existing Git-based development processes, providing a structured layer for managing ML artifacts, pipelines, and experiments across the model lifecycle.