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AI GLOSSARY · DEFINITIONS · LEARNING

Understand the terms behind a production AI lifecycle.

This glossary explains the language used across AI development, training, evidence, deployment and enterprise operations. Definitions are written for builders and decision-makers, not only infrastructure specialists.

AI glossaryAI provenance meaningmodel checkpoint meaningAI benchmark meaningAI deployment terms
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Evidence-awareProduct claims follow current verified platform state.
Human authorityHigh-risk actions remain explicitly approval-gated.
End-to-end AI lifecycleBuild, verify, benchmark, deploy and commercialize.

Core artifact terms

An AI artifact is a versioned object such as a model, dataset, checkpoint, tokenizer or application. A revision is an immutable state of that artifact. A checkpoint is a saved training state that can later be evaluated or continued.

CAS

Content-addressed storage identifies immutable content by cryptographic hashes rather than filenames.

Provenance

A graph of where an artifact came from, what it depends on and what depends on it.

Benchmark

A defined evaluation executed against a specific subject, ideally with retained case evidence.

Verification

A governed review and decision tied to current evidence.

Trust Passport

A signed summary of approved verification scopes for a subject.

SSO

Single sign-on connects an organization identity provider to an application session.

Evidence and trust terms

Provenance describes lineage. Verification is a governed decision against evidence. A benchmark is a repeatable evaluation. A Trust Passport summarizes approved scopes for a verified subject.

Infrastructure and operations terms

GPU cloud refers to rentable accelerator capacity. CAS means content-addressed storage, where content identity is derived from hashes. A managed endpoint is a provider-owned runtime used to serve an application or model through an API.

Common questions about AI Glossary

What is the difference between provenance and verification?

Provenance records lineage and evidence relationships. Verification is an approval decision made using evidence.

What is the difference between a model checkpoint and a release?

A checkpoint is a saved training state. A release is a deliberately selected, evidence-bound version prepared for consumption or production.

Why are there so many AI lifecycle terms?

Production AI combines software engineering, data, model training, infrastructure, security and governance. A shared vocabulary helps teams make the boundaries explicit.

Related AI Hub topics

AI ProvenanceBuild signed AI provenance graphs linking repositories, revisions, Git, CAS, datasets, environments, checkpoints, benchmarks, verification and releases.AI VerificationVerify AI artifacts against current evidence, issue signed verification receipts and govern Trust Passports without converting self-claims into approval.AI BenchmarkingRun reproducible AI benchmarks against immutable revisions with case-level evidence, signed results, worker execution and currentness checks.AI Model RepositoryVersion AI source, models, checkpoints and datasets with real Git plus encrypted content-addressed storage, semantic artifact indexing and signed repository history.GPU CloudPlan and govern rentable GPU capacity for AI training, evaluation and inference with provider connections, job lifecycle, metering and cost controls.
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