Machine-Learning
Machine-Learningmachinelearning
Providers using this tag (1099)
Ranked by API Evangelist rating — Exemplar and Strong are expanded by default.
Exemplar 10 Complete, well-documented, and agent-ready
Strong 52 Solid coverage with minor gaps
Developing 184 Usable, with meaningful gaps to close
Thin 169 Limited public surface area
Emerging 241 Early or largely undocumented
Minimal 410 Almost no public developer surface
Unrated 33 Not yet scored
APIs with this tag (147)
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 13 Complete, well-documented, and agent-ready
Strong 30 Solid coverage with minor gaps
Developing 32 Usable, with meaningful gaps to close
Thin 23 Limited public surface area
Emerging 27 Early or largely undocumented
Minimal 21 Almost no public developer surface
Companies reaching this through an API (39)
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
Related tags
Where this tag comes from
Cohort brief
Auto-generatedThe 996 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 | 29.1 | 23.4 | +5.7 | 996 |
| Discoverability | 62.2 | 61.2 | +1 | 996 |
| Contract Governance | 6.6 | 6.4 | +0.2 | 996 |
| Operational Transparency | 13.9 | 13.9 | 0 | 996 |
| Contract Quality | 18.6 | 19.8 | -1.2 | 996 |
| Access Clarity | 25.2 | 26.5 | -1.3 | 996 |
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) | 13% | 10% | +3 |
| MCP server (first-party) | 14% | 13% | +1 |
| Agent Skills | 0% | 0% | 0 |
| OAuth scopes | 8% | 11% | -3 |
| Security | 97% | 96% | +1 |
| Arazzo workflows | 3% | 2% | +1 |
| Governance rules | 11% | 14% | -3 |
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 Anthropic 77.8
- 2 Elastic Stack (ELK Stack) 76.7
- 3 Amazon CodeGuru Profiler 73
- 4 Qlik Sense 70.5
- 5 Densify 69.7
- 6 MATLAB 69.7
- 7 Oracle Platforms 69.5
- 8 Google Analytics 69
- 9 Edge Impulse 68
- 10 Segmind 67.2
- 1 Bria 58.9
- 2 MOLOCO 57
- 3 AIMLAPI 55.1
- 4 Heuritech 54.4
- 5 Roboflow 54
- 6 The San Francisco Compute Company 53.4
- 7 Fastino Labs 52
- 8 Celonis 51.8
- 9 Hugging Face Transformers 51.7
- 10 Kensho 51.1
Work with this as data
Every tag here is available over the APIs.io API and to AI agents over MCP.