MLOps website screenshot

MLOps

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.

MLOps is profiled on the APIs.io network. Tagged areas include AI Operations, CRISP-ML(Q), DevOps, Machine Learning, and ML Engineering.

The MLOps catalog on APIs.io includes 1 JSON-LD context and 1 Spectral governance ruleset.

🌐 Visit website 📡 Source on GitHub
19.4/100 emerging ▬ flat Agent 0/100 human only Full breakdown ↓
scored 2026-07-27 · rubric v0.5
0 APIs
AI OperationsCRISP-ML(Q)DevOpsMachine LearningML EngineeringML GovernanceML PipelinesModel DeploymentModel MonitoringModel Serving

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-07-27 · rubric v0.5
Composite quality — 19.4/100 · emerging
Contract Quality 3.8 / 25
Developer Ergonomics 0.0 / 20
Commercial Clarity 0.0 / 20
Operational Transparency 0.0 / 13
Governance 8.8 / 12
Discoverability 6.8 / 10
Agent readiness — 0/100 · human only
Machine-Readable Contract 0 / 18
Agentic Access Contract 0 / 15
MCP Server 0 / 12
Machine-Readable Auth 0 / 10
Idempotency 0 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 0 / 7
Rate-Limit Signaling 0 / 7
Typed Event Surface 0 / 6
Agent Skills 0 / 5
Well-Known Catalog 0 / 4
Consent & Bot Identity 0 / 3
Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. This rating is computed from github.com/api-evangelist/mlops: open an issue to ask a question, or submit a pull request to add artifacts. Want it done for you? Prioritized profiling — $2,500 →

Semantic Vocabularies 1

JSON-LD contexts and semantic vocabularies used across these APIs.

Mlops Context

8 classes · 0 properties

JSON-LD

Spectral Rules 1

Spectral governance rulesets for linting and validating these APIs.

MLOps API Rules

5 rules · 3 warnings 2 info

SPECTRAL

JSON Schema 2

Standalone JSON Schema definitions for this provider's data models.

MLOps Model

13 properties

JSON SCHEMA

MLOps Pipeline

9 properties

JSON SCHEMA

Security Posture 1

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Mlops Domain Security

TLSv1.3

SECURITY

Resources

Documentation 3

Reference material describing how the API behaves

References
References
 JSONSchema
JSONSchema
 JSONSchema
JSONSchema

Design & Contract 2

Pagination, idempotency, versioning, errors, and events

ML Workflow Lifecycle
ML Workflow Lifecycle
 JSONLD
JSONLD

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity
DomainSecurity

Operate 1

Status, limits, changes, and where to get help

MLOps Stack Canvas
MLOps Stack Canvas

Commercial 1

Pricing, plans, and the legal terms of use

License
License

Company 1

The organization behind the API

Website
Website

Other 8

Properties that don't map to a standard resource type

Motivation
Motivation
 Designing ML Software
Designing ML Software
 Three Levels of ML Software
Three Levels of ML Software
 MLOps Principles
MLOps Principles
 CRISP-ML(Q)
CRISP-ML(Q)
 ML Model Governance
ML Model Governance
 State of MLOps
State of MLOps
 Publisher
Publisher

Scroll for all 8

Source (apis.yml)

apis.yml Raw ↑ 
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
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
created: '2025'
modified: '2026-04-28'
specificationVersion: '0.20'
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