Create and manage deployments

Serve an evaluated model or workflow through managed, provider-backed, local, or edge infrastructure.

Source reviewed 2026-09-23. Availability depends on your installation, permissions, and compatible runtimes. A supported path is not a guarantee of model quality or production readiness.

Outcome

Create a routable production release with an identifiable artifact, runtime, health state, and rollback path.

Start here

What you need to know first

Check the inputs below before starting. If you are new, begin with the first-project guide. A dataset holds media and labels; a model produces results; a deployment makes a selected model version callable. Creating one does not create the others.

Bring these inputs

An eligible model or workflow version
Runtime image, environment, capacity, and routing configuration

Expected output and limits

A versioned deployment
Endpoint/device assignment, health, and rollback history

Core workflow

01
Choose the release
Select the model or workflow version. Score Studio finds and pins its newest qualifying held-out test; failed or missing evidence cannot receive traffic.
02
Select a target
Choose managed cloud, a connected provider, a local runtime, or a registered device.
03
Configure serving
Set warm replicas, maximum replicas, per-replica concurrency, request timeout, routing, environment, and runtime-specific options. Start below the measured saturation point and repeat the external-client load test after changing capacity.
04
Verify the endpoint
The deployment overview shows Start, Verify runtime, or Use endpoint according to its state. Pause stays directly available for running endpoints; More contains Stop, rollback, and routine verification. Verify runtime health, then create a key restricted to this deployment from API quickstart. Test the endpoint from a separate client, retain request IDs, confirm the pinned model version, and exercise authentication, validation, throttling, and overload behavior. Actions that run away from a form (verifying or deleting a deployment, archiving or deleting dataset files, changing a dataset's visibility) report a failure through a notification in the corner of the page; the page itself stays usable.

What this surface supports

Managed APIs
Deployment-scoped credentials
Correlated success and error logs
Provider-backed serving
Local runtimes
Edge targets

Expert section

Contracts, signals, and failure modes

Use this section when you are defining acceptance criteria, automating the surface, or reviewing whether its output is safe to promote downstream.

Quality and operating signals

Readiness and liveness
Traffic, saturation, and errors
Latency and cost per inference

Common failure modes

Missing runtime dependency
Insufficient capacity
Credential or network failure
Health check that does not exercise inference

Expert release checklist

Inputs and dependencies are pinned to immutable versions.
Acceptance metrics include critical classes and operating slices.
Failure, retry, cost, and rollback behavior are understood.
The resulting artifact has an owner and a downstream review path.

Engineering safeguards

A stopped Score deployment can be prepared without spend; traffic waits for a passing exact-version test
Runtime health and model-quality evidence remain separate
Authenticated endpoint failures retain the response request ID in deployment logs
Rollback keeps artifact history