Documentation

Analyze an image or clip, review labels, train a model, and test it before deployment. Start with the product guide if you are new to Score Studio. Each guide explains inputs, actions, results, and availability limits.

30feature guides

07lifecycle areas

01live API contract

Start

02 guides

Run Score Studio locally

Run the Apache 2.0 software on infrastructure you operate. You supply storage and inference; help comes through community docs and discussions.

Understand Score Studio

Start with an image, a clip, or existing data. Learn what is saved, what runs automatically, and what needs your approval.

Workflows

03 guides

Design and orchestration

Build a production workflow

Compose the complete vision lifecycle instead of treating model training as the final destination.

Durable execution

Run and control workloads

Follow annotation, model analysis, evaluation, and workflow jobs with saved progress and results.

Preview and tracked runs

Choose the right execution mode

Use Preview for transient inspection and a tracked workflow for saved labels, artifacts, approvals, deliveries, and receipts.

Data

05 guides

Versioned evidence

Build and inspect datasets

Create specialist or multimodal datasets around the outcome the system must understand.

Label and review

Use Annotation studio

Draw or correct object labels on images and sampled frames, then explicitly confirm their review.

Import and export annotations

Move labels and multimodal training evidence between Score and external systems.

Statistics and quality

Diagnose dataset health

Use coverage, balance, dimensions, spatial plots, and split statistics to find data risk before training.

Asset operations

Manage dataset assets

Filter, archive, restore, and permanently remove versioned media and annotations safely.

Models

09 guides

Artifacts and foundations

Choose and inspect models

Choose a ready specialist model or an off-the-shelf vision-language foundation, then inspect the exact runtime and version.

Availability limit

Understand VLM training availability

Multimodal dataset schemas do not imply an executable vision-language model trainer. General VLM fine-tuning is not an implemented training path in this release.

Train a model to outline objects

Segmentation learns object shapes. Training your own model is separate from using the built-in segmentation service.

Experimental · local CPU

Optimize vision-model weights with rewards

This experimental path uses reviewed annotations to reward sampled model outputs and update the vision model itself.

When training stops

Recover a failed training attempt

Find the cause and keep the exact data selection instead of starting another run blindly.

Train and fine-tune models

Train from reviewed boxes or object outlines. Detection candidate search uses a configured engine; segmentation and experimental reward learning run locally.

Quality evidence

Evaluate a model release

Measure one executable model version on a declared benchmark or custom held-out dataset with frozen thresholds, metrics, uncertainty, and sample evidence.

Frontier model work

Commission The Vision Lab

Ask Score to build a frontier or unusually complex visual model when existing foundations are insufficient.

Interactive validation

Test a model interactively

Run representative visual inputs through a selected model before committing it to a workflow or release.

Operate

05 guides

Release and runtime

Deploy and operate models

Release models to devices, providers, managed APIs, or outcome-based workloads.

Continuous assurance

Run conformity and monitoring

Check live behavior against a declared production contract and investigate drift or policy failures.

Create and manage deployments

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

On-device runtime

Register and operate edge devices

Connect owned hardware for local inference, private processing, and device-backed workloads.

Global feedback

Track and control tasks

Follow every long-running operation from one persistent workspace task center.

Developers

04 guides

External systems

Connect providers and integrations

Connect infrastructure once. Score verifies it and publishes its exact capabilities to every compatible product surface.

Build with the SDK and REST API

Automate the same data-to-production lifecycle used in the Score Studio interface.

Connect agents with controlled execution

Connect an MCP-compatible agent to workspace-scoped datasets, models, evaluations, and deployments.

REST API reference

Inspect schemas and execute authenticated API operations against the configured Score Studio API.

Workspace

02 guides

Plans and spend

Manage billing and usage

Understand plan allowances, managed AI capacity, usage, payments, and invoices.

Access and governance

Manage workspace access and security

Control members, API keys, workspace identity, sign-in methods, and security-sensitive settings.

Product guides are versioned with Score Studio so behavior, safeguards, and documentation ship together.

Documentation · Score Studio