The outcome

Deploy a useful ML demonstration without exposing code, credentials, data, or uncontrolled costs.

Step by step

A workflow you can repeat.

  1. 01

    Define the demo audience, inputs, outputs, model license, abuse cases, resource limits, and data-handling notice.

  2. 02

    Choose Gradio, Docker, or static HTML and select public, protected, or private visibility deliberately.

  3. 03

    Build locally with pinned dependencies, input validation, clear errors, and representative example data.

  4. 04

    Store credentials as Space secrets, never variables, and test cold starts, concurrency, timeouts, and large inputs.

  5. 05

    Review public source exposure, logs, analytics, hardware cost, moderation, and ownership before sharing the URL.

Working standard

What good use looks like.

  • Assume public Space code is visible.
  • Keep secrets out of commits.
  • Set cost and abuse guardrails.

Official references

Check the current product documentation.