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Build with the SDK and REST API

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

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 reproducible scripts, CI jobs, and external applications against workspace-scoped contracts.

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

  • Workspace base URL and scoped bearer token
  • Validated request bodies with version IDs and idempotency strategy

Expected output and limits

  • Typed resources and durable task IDs
  • Structured errors suitable for retry and observability

Core workflow

  1. 01

    Create an API key

    Open a deployment → API quickstart → Create endpoint key to issue an inference-only credential restricted to that endpoint. Use a signed-in session for management routes and separate scoped keys for inference and MCP. Keys are not interchangeable with sessions and their secret is shown once.

  2. 02

    Configure the client

    Set the base URL and bearer token, then verify API status.

  3. 03

    Call lifecycle primitives

    Create datasets and versions, launch runs, evaluate artifacts, deploy, and query tasks.

  4. 04

    Handle durable work

    Poll or subscribe to task state. For inference, preserve request_id and model_version_id, retry only explicit transient statuses with a bound, and use the request ID to correlate failures with deployment logs.

What this surface supports

  • Python client with JSON responses
  • OpenAPI REST contract
  • Route-specific session or API-key authentication
  • Durable task endpoints

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

  • HTTP status and error code
  • Rate-limit headers
  • Task state and request correlation ID

Common failure modes

  • Leaking tokens
  • Polling without backoff
  • Retrying mutations without idempotency
  • Using names where immutable IDs are required

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

  • Use least-privilege keys
  • Never embed secrets in clients
  • Treat asynchronous IDs as durable references
Build with the SDK and REST API · Score Studio