Machine Learning
MLMachine-Learning
Providers using this tag (1106)
Ranked by API Evangelist rating — Exemplar and Strong are expanded by default.
Exemplar 15 Complete, well-documented, and agent-ready
Strong 55 Solid coverage with minor gaps
Developing 173 Usable, with meaningful gaps to close
Thin 168 Limited public surface area
Emerging 228 Early or largely undocumented
Minimal 448 Almost no public developer surface
APIs with this tag (148)
Ranked by the provider's API Evangelist rating — the Kin Score is scored per provider, not per API, so every API of a provider shares its band. How the rating works →
Exemplar 28 Complete, well-documented, and agent-ready
Strong 20 Solid coverage with minor gaps
Developing 27 Usable, with meaningful gaps to close
Thin 24 Limited public surface area
Emerging 28 Early or largely undocumented
Minimal 21 Almost no public developer surface
Companies reaching this through an API (42)
These companies publish an API, specification or operation carrying “Machine Learning” but do not classify their business under it. Listed unranked and kept out of the count above, because one tagged operation is not a statement about what a company does.
Score breakdown
Where this tag sits
1106 providers carry this tag directly, and 1149 counting the 8 narrower tags below — 43 more to reach.
Related tags
Where this tag comes from
Cohort brief
Auto-generatedThe 1013 providers in the APIs.io catalog tagged Machine Learning, scored on the Kin Score. Every figure below is computed from the catalog — nothing here is written.
| Facet | This cohort | Catalog | Difference | Scored |
|---|---|---|---|---|
| Developer Ergonomics | 28.9 | 23.2 | +5.7 | 1013 |
| Discoverability | 59.1 | 58.1 | +1 | 1013 |
| Contract Governance | 6.5 | 6.3 | +0.2 | 1013 |
| Operational Transparency | 13.9 | 13.7 | +0.2 | 1013 |
| Contract Quality | 16.7 | 17.6 | -0.9 | 1013 |
| Access Clarity | 25.5 | 26.5 | -1 | 1013 |
A facet is averaged over the members that carry it, not over the whole cohort — the “Scored” column is that count. Averaging an absent facet as zero would score our own coverage gaps as the providers’ posture.
| Artifact | This cohort | Catalog | Difference |
|---|---|---|---|
| MCP server (any) | 15% | 14% | +1 |
| MCP server (first-party) | 13% | 12% | +1 |
| Agent Skills | 0% | 0% | 0 |
| OAuth scopes | 8% | 11% | -3 |
| Security | 98% | 98% | 0 |
| Arazzo workflows | 3% | 2% | +1 |
| Governance rules | 12% | 14% | -2 |
mcp_pct counts any mcp/ artifact including ones API Evangelist derived from the provider OpenAPI; mcp_first_party_pct counts only servers the provider publishes. Prefer the latter.
- 1 ElevenLabs 81.8
- 2 Anthropic 79.4
- 3 Elastic Stack 78
- 4 Amazon CodeGuru Profiler 73.6
- 5 MATLAB 72.4
- 6 Qlik Sense 72
- 7 Cloudera 70.3
- 8 Google Analytics 70.2
- 9 Densify 69.5
- 10 Edge Impulse 69.4
- 1 Bria 65
- 2 MOLOCO 57
- 3 Runway 56.4
- 4 AIMLAPI 55.1
- 5 Roboflow 54
- 6 Prior Labs 53.8
- 7 The San Francisco Compute Company 53.4
- 8 ElevenLabs 52.7
- 9 Fastino Labs 52
- 10 Celonis 51.8
Work with this as data
Every tag here is available over the APIs.io API and to AI agents over MCP.