🔢Versioning

NEW

Also known as: Software Versioning

Assigning identifiers to different states of artifacts to track changes and compatibility.

DESCRIPTION

Versioning labels artifacts—code, data, models, APIs—with identifiers to manage evolution. It enables traceability, reproducibility, dependency resolution, and compatibility guarantees. Practices include semantic versioning for APIs and packages, data snapshots, and model registry versions. Governance defines increment rules, deprecation policies, and documentation. Tools like Git and DVC operationalize version control for software and data workflows.

ALTERNATIVE DEFINITIONS

Wikipedia

Software versioning is the process of assigning distinct version names or numbers to unique states of computer software.

Focuses specifically on software artifacts and unique states.

Source

SemVer

Given a version number MAJOR.MINOR.PATCH, increment the MAJOR version when you make incompatible API changes, the MINOR version when you add functionality in a backwards compatible manner, and the PATCH version when you make backwards compatible bug fixes.

Defines a widely used scheme and its increment rules.

Source

Git

Version control is a system that records changes to a file or set of files over time so that you can recall specific versions later.

Explains the purpose of recording and recalling versions over time.

Source

RELATED TERMS

Version Control Data Versioning Model Versioning Git Data Version Control Schema Evolution API Versioning Releasing Audit Trail Immutable Artifact

COMPETING TERMS

In-place Updates

RELATIONS

• Narrower: Coexistence Style Data Versioning Model Versioning Ontology Versioning Schema Versioning Semantic Versioning

KNOWLEDGE AREAS

Software Engineering Data Engineering MLOps Metadata Management

TARGET AUDIENCE

DevOps Engineer Software Developer Data Engineer MLOps Engineer AI Engineer Data Steward

EXTERNAL SOURCES

• Wikipedia - Software versioning
• Semantic Versioning - SemVer specification
• Git - Pro Git book chapters
• DVC - Data versioning documentation

TOP QUESTIONS

Why is Versioning critical in data and AI projects?

It ensures reproducibility, traceability, and safe collaboration. Versions enable rollback and compatibility guarantees.

Category: Relevancy

How does Semantic Versioning help APIs?

It communicates compatibility clearly, making upgrade planning predictable for consumers.

Category: Benefits

What are common versioning mistakes?

Skipping changelogs, breaking compatibility without major increments, and conflating build numbers with release versions are frequent errors.

Category: Mistakes

How should data be versioned?

Use immutable snapshots, lineage metadata, and hash-based identifiers; store schemas alongside data.

Category: How-to

What is the risk of no Versioning?

Without versions, changes become untraceable, reproducibility breaks, and deployments risk regressions.

Category: Risks

How do model registries implement Versioning?

They assign version identifiers, track lineage and metrics, and manage stages like staging and production.

Category: Overview

How to choose version number formats?

Prefer SemVer for libraries; use calendar versions for time-based releases; be consistent and documented.

Category: Best practices

Does Git provide Versioning by itself?

Git tracks changes; releases should be tagged and documented to form meaningful versions.

Category: Comparison

How to manage breaking changes with Versioning?

Use major increments, deprecation notices, migration guides, and dual-support windows.

Category: Management