Deploy a Trained Model
Review a completed training run, create a deployment, and validate the model with a production-style smoke test.
This guide is the handoff from model improvement to production validation.
What you'll have when you finish
- one deployment for the trained model
- one public model identifier
- one successful smoke test against the deployment endpoint
Before you start
- complete a training run with the E2E Fine-tuning guide
- confirm the trained model still beats or matches the baseline on the eval you trust
Step 1: review the completed training job
Before you create a deployment, inspect the training job detail page for:
- final status
- external job ID
- base model
- current or final loss
- checkpoint evals and average scores
- final model reference / weights
Do not promote a model you cannot explain.
Step 2: create the deployment
In the deployment create flow, you choose:
- deployment name (a matching slug is auto-derived and editable)
- model
- instance type (GPU family and cards per instance, e.g. H100 or H200, 1-2 per instance)
- instance count
The dashboard generates the public deployment identifier in the shape orgSlug/slug, where slug is the slugified deployment name you set.
Step 3: copy the public model identifier
Once the deployment exists, copy the deployment's public model identifier from the overview page. You will use that as the model value in a normal API request.
Step 4: send a smoke test
Use the deployment's public model identifier with the standard API shape and verify that:
- the request completes successfully
- the output is correct enough for the workflow
- the request shows up in the deployment inferences view
Step 5: watch the deployed traffic
After rollout, inspect:
- deployment overview
- instances
- recent deployment inferences
- Gateway analytics for the surrounding workflow
The goal is not just “deployment succeeded.” The goal is “the model behaves correctly under real traffic.”
Verify it worked
You should now have:
- one live deployment
- one public model identifier
- one successful deployment request visible in the deployment inferences tab