---
title: "ML Ops"
type: concept
tags: [machine-learning, operations, lifecycle]
sources: [specialized-model-qa]
last_updated: 2026-04-20
---

## Definition
ML Ops is the discipline of operationalizing machine learning models across development, deployment, monitoring, and governance.

## Core Areas
- Data pipelines
- Training and deployment
- Monitoring and drift detection
- Governance and auditability

## Relevance to Model QA
- Provides the operational context for audits
- Supplies monitoring and reproducibility artifacts
- Supports remediation and retraining loops

## Related Concepts
- [[Model Audit]]
- [[Discrimination Metrics]]