stale · -1.1

MLflow

Score 14.1 / 100 4 providers 7 APIs Search apis.io →

Providers using this tag (4)

Ranked by API Evangelist rating — Exemplar and Strong are expanded by default.

Strong 1 Solid coverage with minor gaps
DatabricksCollection of Databricks REST APIs for managing workspaces, clusters, jobs, and data operations.57 APIs59.1
Thin 1 Limited public surface area
DagsHubDagsHub is a GitHub-like platform for ML and data teams that combines code, data (DVC), experiments (MLflow...11 APIs27.1
Emerging 2 Early or largely undocumented
NebiusNebius is an AI-focused cloud platform spun out of Yandex, offering NVIDIA GPU virtual machines and cluster...6 APIs24.3EvalsA landscape catalog of the platforms, frameworks, libraries, and benchmark suites used to evaluate large la...20 APIs16.0

APIs with this tag (7)

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 →

Strong 4 Solid coverage with minor gaps
Databricks Experiments APIThe Databricks Experiments API allows you to create, update, list, and manage MLflow experiments. Experimen...59.1Databricks Model Registry APIThe Databricks Model Registry API provides the workspace model registry for managing the full lifecycle of ...59.1Databricks Model Versions APIThe Databricks Model Versions API allows you to manage model versions within the Unity Catalog model regist...59.1Databricks Registered Models APIThe Databricks Registered Models API allows you to create, update, list, and delete registered models in th...59.1
Thin 1 Limited public surface area
DagsHub MLflow Tracking EndpointEach DagsHub repo provides a hosted MLflow tracking server endpoint. Point `MLFLOW_TRACKING_URI` at the rep...27.1
Emerging 2 Early or largely undocumented
Nebius Managed Applications APIThe Managed Applications API deploys and manages JupyterLab, vLLM, Open WebUI, MLflow, and other ready-made...24.3MLflow LLM EvaluateMLflow LLM evaluate extends MLflow's experiment tracking with mlflow.evaluate() support for LLM tasks. The ...16.0

Score breakdown

Frequency
25.8
log-scaled weighted occurrences
Breadth
0.1
spread across providers
Quality lift
37.9
mean composite of providers using it
Cohesion
0.0
strength of nearest seed neighbor

Where this tag comes from

Provider tag2
Api tag7