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Step 1 — Train a model

  1. Go to app.impulselabs.ai and sign in.
  2. Upload a CSV dataset under Datasets.
  3. Click Train Model, pick your target column, and start training.
  4. Once training finishes, note the Deployment ID shown on the model card — you’ll need it when calling /infer.
Deployment IDs look like clf-titanic-survived or a UUID. You can find them on the Models page in the dashboard.

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Step 2 — Create an API key

  1. In the dashboard, go to Settings → API Keys.
  2. Click New API Key, give it a name (e.g. my-app), and click Create.
  3. Copy the key immediately — it starts with imp_ and is shown only once.
Store your API key securely (e.g. in an environment variable). It cannot be retrieved after creation.
export IMPULSE_API_KEY="imp_your_key_here"
export DEPLOYMENT_ID="your-deployment-id"

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Step 3 — Check deployment status

Before calling /infer, confirm the model is ACTIVE: 
curl -s "https://inference.impulselabs.ai/deploy/status?deployment_id=$DEPLOYMENT_ID" \
  -H "Authorization: Bearer $IMPULSE_API_KEY" | jq .status
Expected response: 
{
  "deployment_id": "your-deployment-id",
  "status": "ACTIVE"
}
If status is CREATED or BUILDING, wait a few seconds and retry — the model container is warming up.

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Step 4 — Run your first inference

Replace the inputs fields with your actual feature column names and values: 
curl -s -X POST "https://inference.impulselabs.ai/infer" \
  -H "Authorization: Bearer $IMPULSE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "deployment_id": "'$DEPLOYMENT_ID'",
    "inputs": {
      "Pclass": 3,
      "Sex": "male",
      "Age": 22,
      "SibSp": 1,
      "Parch": 0,
      "Fare": 7.25,
      "Embarked": "S"
    }
  }'
Response: 
{
  "prediction": 0,
  "probability": 0.142,
  "target": "Survived"
}

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Next steps

Authentication

Manage multiple API keys

Rate limits

Understand limits and headers

Error reference

Handle errors gracefully

Inference API

Full parameter reference