MLOps

aid: mlops
name: MLOps
description: 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. This index curates
  the canonical references, frameworks, and ecosystem resources for practicing MLOps across data engineering, ML pipelines,
  and model serving.
type: Index
deliveryModel:
  model: unknown
  open_source: false
  commercial: false
  callable_host: false
  label: Delivery model not determined — needs a product licence on record
  confidence: low
  source:
  - none
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source: []
  generated: '2026-07-22'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/mlops.png
url: https://raw.githubusercontent.com/api-evangelist/mlops/refs/heads/main/apis.yml
tags:
- AI Operations
- CRISP-ML(Q)
- DevOps
- Machine-Learning
- ML Engineering
- ML Governance
- ML Pipelines
- Model Deployment
- Model Monitoring
- Model Serving
tags_raw:
- 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'
specificationVersion: '0.23'
apis: []
common:
- type: DomainSecurity
  url: security/mlops-domain-security.yml
- type: Website
  url: https://ml-ops.org/
- type: Motivation
  url: https://ml-ops.org/content/motivation
- type: Designing ML Software
  url: https://ml-ops.org/content/phase-zero
- type: ML Workflow Lifecycle
  url: https://ml-ops.org/content/end-to-end-ml-workflow
- type: Three Levels of ML Software
  url: https://ml-ops.org/content/three-levels-of-ml-software
- type: MLOps Principles
  url: https://ml-ops.org/content/mlops-principles
- type: CRISP-ML(Q)
  url: https://ml-ops.org/content/crisp-ml
- type: MLOps Stack Canvas
  url: https://ml-ops.org/content/mlops-stack-canvas
- type: ML Model Governance
  url: https://ml-ops.org/content/model-governance
- type: References
  url: https://ml-ops.org/content/references
- type: State of MLOps
  url: https://ml-ops.org/content/state-of-mlops
- type: Publisher
  url: https://www.innoq.com/
- type: License
  url: https://creativecommons.org/licenses/by/4.0/
- type: JSONLD
  url: json-ld/mlops-context.jsonld
- type: JSONSchema
  url: json-schema/mlops-model-schema.json
- type: JSONSchema
  url: json-schema/mlops-pipeline-schema.json
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
  url: https://apievangelist.com