BigML
BigML is a machine learning platform with a comprehensive REST API for creating datasets, training models, making predictions, running batch predictions, and managing ML workflows. The platform supports supervised and unsupervised learning including decision trees, ensembles, deepnets, linear and logistic regression, clustering, anomaly detection, topic models, and time series forecasting.
Real signal across most facets with visible, nameable gaps — the contract exists but is thin, or the portal is good while governance and commercial terms are absent.
API Evangelist profiles BigML the way a machine reads it — 30 machine-readable artifacts across 15 APIs, 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 — BigML scores 56.2/100 (developing), with a separate agent-readiness read of 48/100 (agent ready). 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 BigML
Each block below is one kind of artifact we hold for BigML. 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.
APIs 15
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
BigML Anomaly Detection API
Detect anomalies in datasets using Isolation Forest
BigML Batch Operations API
Run predictions, centroid assignments, and anomaly scoring on full datasets
BigML Clustering API
Create unsupervised cluster models and assign centroids
BigML Data Connectors API
Connect to external databases and data sources
BigML Datasets API
Create and manage training datasets from sources
BigML Ensembles API
Train and manage ensemble models (random forests, gradient boosted trees)
BigML Evaluations API
Evaluate model performance against a test dataset
BigML Models API
Train and manage decision tree models
BigML Predictions API
Generate individual predictions from trained models
BigML Projects API
Organize resources into projects
BigML Sources API
Upload and manage raw data sources (CSV, JSON, Excel, etc.)
BigML Supervised Learning API
Logistic regression, linear regression, and deep neural network models
BigML Time Series API
Time series forecasting models and forecasts
BigML Unsupervised Learning API
Topic models, association rules, and PCA
BigML WhizzML Scripting API
Automate ML workflows with WhizzML scripts and executions
Scroll within the panel for all 15 ·
Pricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Bigml Plans Pricing
PLANSRate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Bigml Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Bigml Finops
FINOPSSemantic 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.
Bigml 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.
BigML API Rules
SPECTRALJSON Schema 1
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.
BigML Resource
JSON SCHEMAExamples 6
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Create Cluster
EXAMPLECreate Dataset
EXAMPLECreate Ensemble
EXAMPLECreate Model
EXAMPLECreate Prediction
EXAMPLECreate Source
EXAMPLESecurity Posture 2
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.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Resources
Every other property we hold for BigML — 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 1
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Build 1
SDKs, sample code, and the tooling you integrate with
Access & Security 2
Authentication, authorization, and security posture
Operate 2
Status, limits, changes, and where to get help
Commercial 3
Pricing, plans, and the legal terms of use
Company 3
The organization behind the API
Other 1
Properties that don't map to a standard resource type
← All providers · Data indexed from github.com/api-evangelist/bigml · machine-readable index on apis.io