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.
More than an index entry, but the surface is still mostly links rather than artifacts — the cohort most likely to move a full band from modest, well-targeted work.
API Evangelist profiles MLOps the way a machine reads it — 5 machine-readable artifacts, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — MLOps scores 19.4/100 (emerging), with a separate agent-readiness read of 0/100 (human only). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile MLOps
Each block below is one kind of artifact we hold for MLOps. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
Semantic Vocabularies 1
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Mlops Context
JSON-LDSpectral Rules 1
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
MLOps API Rules
SPECTRALJSON Schema 2
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
MLOps Model
JSON SCHEMAMLOps Pipeline
JSON SCHEMASecurity Posture 1
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Resources
Every other property we hold for MLOps — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Documentation 3
Reference material describing how the API behaves
Design & Contract 2
Pagination, idempotency, versioning, errors, and events
Access & Security 1
Authentication, authorization, and security posture
Operate 1
Status, limits, changes, and where to get help
Commercial 1
Pricing, plans, and the legal terms of use
Company 1
The organization behind the API
Other 8
Properties that don't map to a standard resource type
Scroll within the panel for all 8 ·
← All providers · Data indexed from github.com/api-evangelist/mlops · machine-readable index on apis.io