MLOps (Machine Learning Operations) is an engineering discipline that unifies machine learning development with operations to deploy, monitor, govern, and maintain machine learning models in production. MLOps.org publishes an end-to-end reference for designing, building, and managing reproducible, testable, and evolvable ML-powered software, including the CRISP-ML(Q) process model, the MLOps Stack Canvas, MLOps principles, and ML model governance practices.
URL: Visit APIs.json URL
- Type: Index
- Position: Consuming
- Access: 3rd-Party
- AI Operations
- CRISP-ML(Q)
- DevOps
- Machine Learning
- ML Engineering
- ML Governance
- ML Pipelines
- Model Deployment
- Model Monitoring
- Model Serving
- Created: 2025
- Modified: 2026-04-28
| Type | File | Description |
|---|---|---|
| JSON-LD | json-ld/mlops-context.jsonld | Linked-data context mapping MLOps entities to schema.org and an MLOps vocabulary |
| JSON Schema | json-schema/mlops-model-schema.json | Schema describing a registered ML model artifact, framework, metrics, and lifecycle stage |
| JSON Schema | json-schema/mlops-pipeline-schema.json | Schema describing an end-to-end MLOps pipeline with ingest, train, deploy, and monitor stages |
- Website
- Motivation
- Designing ML Software
- ML Workflow Lifecycle
- Three Levels of ML Software
- MLOps Principles
- CRISP-ML(Q)
- MLOps Stack Canvas
- ML Model Governance
- References
- State of MLOps
- Publisher (INNOQ)
- License (CC BY 4.0)
FN: Kin Lane Email: kin@apievangelist.com URL: https://apievangelist.com