GitOps
An operational framework where Git repositories serve as the single source of truth for infrastructure and application configuration, with automated agents ensuring the live environment matches the declared state.
GitOps extends infrastructure as code by making Git the operational control plane. All changes to infrastructure and application configuration are made through pull requests. Automated agents (like ArgoCD or Flux) continuously monitor the Git repository and reconcile the live environment to match the declared state, automatically applying any drift corrections.
The workflow is straightforward: a developer opens a pull request to change a Kubernetes manifest, the change is reviewed and merged, and the GitOps agent detects the change and applies it to the cluster. Rollbacks are as simple as reverting the Git commit. The entire history of every infrastructure change is captured in the Git log.
For AI teams, GitOps provides auditability and reproducibility for model deployments. Model versions, serving configurations, feature flag states, and pipeline definitions all live in Git. This means you can answer questions like "what model was serving traffic last Tuesday" by examining the Git history, and you can reproduce any previous system state by checking out that commit.
Related Terms
A/B Testing
A controlled experiment comparing two or more variants to determine which performs better on a defined metric, using statistical methods to ensure reliable results.
Feature Flag
A software mechanism that enables or disables features at runtime without deploying new code, used for gradual rollouts, A/B testing, and targeting specific user segments.
MLOps
The set of practices combining machine learning, DevOps, and data engineering to reliably deploy, monitor, and maintain ML models in production.
Model Serving
The infrastructure and systems that host trained ML models and handle inference requests in production, optimizing for latency, throughput, and cost.
Semantic Search
Search that understands the meaning and intent behind a query rather than just matching keywords, typically powered by embedding-based similarity comparison.
CI/CD (Continuous Integration / Continuous Deployment)
An automated software practice where code changes are continuously integrated into a shared repository, tested, and deployed to production, reducing manual intervention and accelerating delivery cycles.