Semantic Versioning
A versioning system (MAJOR.MINOR.PATCH) that conveys meaning about the underlying changes. In AI, it applies to model versions, API versions, and prompt versions.
Why It Matters
Semantic versioning prevents breaking changes from surprising users. Knowing that v2.0 has breaking changes while v1.3 is backward-compatible enables informed decisions.
Example
Model API v1.2.3: patch 3 (bug fix), minor version 2 (new capability, backward-compatible), major version 1 (established interface).
Think of it like...
Like software version numbers that tell you if an update is a small fix (x.x.1), a new feature (x.1.x), or a major overhaul (2.x.x).
Related Terms
API
Application Programming Interface — a set of rules and protocols that allow different software applications to communicate with each other. In AI, APIs let developers integrate AI capabilities into their applications.
Deployment
The process of making a trained ML model available for use in production applications. Deployment involves packaging the model, setting up serving infrastructure, and establishing monitoring.
Model Registry
A centralized repository for storing, versioning, and managing trained ML models along with their metadata (metrics, parameters, lineage). It serves as the system of record for models.
MLOps
Machine Learning Operations — the set of practices that combine ML, DevOps, and data engineering to deploy and maintain ML models in production reliably and efficiently.