Get startedCompose the complete vision lifecycle instead of treating model training as the final destination.
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
- Typed datasets, model versions, prompts, or upstream outputs
- Runtime, policy, and acceptance constraints
Expected output and limits
- An immutable workflow revision
- Typed stage outputs and launchable workload configuration
Core workflow
- 01
Try a working example
On an empty Workflows page, choose Find and count objects. Score Studio creates an image → public detector → count → output graph. Select one of the published COCO sample images and run the preview to inspect real detections and the final count. The example starts no training or deployment.
- 02
Open the workflow from its project
Every project shows its lifecycle from data through monitoring. Choose Open visual workflow to open the matching graph. If the project has no graph yet, Score Studio creates one from its saved task, dataset version, and runnable model version; returning to the project preserves unsaved-change protection.
- 03
Repair setup issues
When repairs can be applied, choose Auto-fix workflow to repair and save the setup immediately, or Review issues to inspect it first. If no automatic repair can be applied, Review issues opens the steps that need your input, with a button to open the next step. Auto-fix repairs connections and fills missing settings from the project model, selected dataset, or a single compatible healthy resource. It preserves existing choices and never starts a run. The result lists applied changes and unresolved steps. Undo is available until you change the repaired graph.
- 04
Choose a system objective
Choose a task and inspect each block’s runtime availability. A template or task name does not prove an executable adapter is installed.
- 05
Bind versioned inputs
Attach datasets, pretrained registry models, prompts, policies, or upstream workflow outputs.
- 06
Add lifecycle stages
Configure optional training or fine-tuning, evaluation gates, deployment, monitoring, and feedback.
- 07
Launch a workload
Run the reusable definition against real inputs with progress, spend, logs, and outputs attached.
What this surface supports
- Task-specific blocks with installed runtimes
- Pretrained-to-production paths
- Fine-tuning and feedback loops
- Preview and release stages
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.