Get startedFollow annotation, model analysis, evaluation, and workflow jobs with saved progress and results.
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.
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
- A workflow or supported operation
- Versioned inputs and a selected runtime
Expected output and limits
- Durable task records
- Artifacts, logs, usage, and reviewable evidence
Core workflow
- 01
Choose completed work
Start an annotation, inference, evaluation, or reusable workflow outcome.
- 02
Select inputs and runtime
Bind immutable versions and choose Score-managed credits, a provider, or your own device.
- 03
Control execution
Open Build & evaluate → Runs for the full run history, or Runs in the bottom bar for quick progress. Open a run to inspect its status and use pause, resume, or cancel only when supported.
- 04
Inspect the result
Follow the task back to its source page and retain outputs, logs, spend, and review evidence.
What this surface supports
- Active and recent runs
- Progress and task deep links
- Multi-model annotation
- Managed-credit execution
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.
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.