Models
The Schema models you can run on and create endpoints on, how to choose a base, and how an upgrade moves an endpoint to a newer base.
Data and Endpoints are the objects you create; every self-serve operation is inference. “Model” always means a Schema base.
The model registry is the documentation of record for every Schema model, current and retired: intended use, training data, evaluation, lifecycle status, and known limitations. GET /v2/models returns the bases available to your org and which one is the default, plus any Enterprise fine-tune you own; lifecycle, evaluations, and task support live on each model card in the registry.
Choosing a base
Use the latest Schema model: it is what runs and new endpoints use when base is omitted, and it supports all three prediction task types (classification, regression, anomaly). Name a specific base only when you need a fixed comparison point.
A retired model answers runs, creations, and serves with 422 capability_unavailable. An endpoint created on it keeps that base until you move it with POST /v2/endpoints/:id/upgrade (see Endpoints); its earlier reports keep their base name, so the trail stays readable.
Bases are named schema-{n} (s{n} in report names); the API version /v2/ is a separate axis. See Identifiers.
Enterprise fine-tunes
Fine-tuning is an Enterprise program. A fine-tune is served behind an ordinary endpoint you own, bills as ordinary inference, and carries the same held-out report contract. Contact sales.