Engine ML
Engine ML was a machine-learning infrastructure startup (founded 2018, backed by Kleiner Perkins) that offered a hosted platform for distributed model training and experiment tracking. Its product surface centered on the `engine` command-line tool and the `eml` Python library, which let data scientists launch experiments, track code, system utilization, logs, and output files, and run image classifiers (PyTorch/TensorFlow/Keras) against datasets such as MNIST. The company appears to be defunct: engineml.com serves an empty page behind an expired TLS certificate (expired March 2023), the documentation site docs.engineml.com returns an empty S3 bucket, and the public GitHub organization (EngineML) has not been updated since 2020-2021. No live API, OpenAPI description, package-registry release, or reachable developer docs could be found. This profile preserves the historical identity and the still- live GitHub organization as the only verifiable surviving surface.
Index entry only — little beyond a description and a link, and nothing machine-readable enough for an agent to act on without a human reading the site first.
API Evangelist profiles Engine ML the way a machine reads it — 1 machine-readable artifact, 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 — Engine ML scores 7.4/100 (minimal), 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 Engine ML
Each block below is one kind of artifact we hold for Engine ML. 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.
Security 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 Engine ML — 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.
Build 1
SDKs, sample code, and the tooling you integrate with
Access & Security 1
Authentication, authorization, and security posture
Company 1
The organization behind the API
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