API Evangelist conversational phrasing for Amazon Neptune Neptune ML Inference Endpoints API

# Generated by API Evangelist (build-phrasing.py). Our phrasing, not observed demand.
overlay: 1.0.0
info:
  title: API Evangelist conversational phrasing for Amazon Neptune Neptune ML Inference Endpoints API
  version: 1.0.0
extends: openapi/amazon-neptune-inference-endpoints-api-openapi.yml
actions:
- target: $.info
  update:
    x-apievangelist-phrasing:
      method: generated
      generated: '2026-10-01'
      generator: build-phrasing.py
      label: Generated by API Evangelist
      operations: 4
- target: $.paths['/ml/endpoints'].get
  update:
    x-apievangelist-phrasing:
      intent: List active ML inference endpoints
      effect: read
      questions:
      - Which Neptune ML inference endpoints are active?
      - Can I limit how many inference endpoint IDs are returned?
      instructions:
      - text: List my active Neptune ML inference endpoints.
      - text: Show up to {max} inference endpoint IDs.
        slots:
          max: query.maxItems
      method: generated
      generated: '2026-10-01'
- target: $.paths['/ml/endpoints'].post
  update:
    x-apievangelist-phrasing:
      intent: Deploy an ML inference endpoint
      effect: write
      questions:
      - How do I deploy a trained Neptune ML model for real-time predictions?
      - Can I update an existing inference endpoint with a newer model?
      - What instance type and count can I pick for an inference endpoint?
      instructions:
      - text: Create an inference endpoint from model training job {training_job}.
        slots:
          training_job: requestBody.mlModelTrainingJobId
      - text: Deploy an inference endpoint {id} from transform job {transform_job} on {instance}.
        slots:
          id: requestBody.id
          transform_job: requestBody.mlModelTransformJobId
          instance: requestBody.instanceType
      - text: Update inference endpoint {id} with training job {training_job}.
        slots:
          id: requestBody.id
          training_job: requestBody.mlModelTrainingJobId
      method: generated
      generated: '2026-10-01'
- target: $.paths['/ml/endpoints/{id}'].get
  update:
    x-apievangelist-phrasing:
      intent: Check an ML inference endpoint's status
      effect: read
      questions:
      - Is my Neptune ML inference endpoint ready to serve predictions?
      - What state is a given inference endpoint in?
      instructions:
      - text: Get the status of inference endpoint {id}.
        slots:
          id: path.id
      - text: Check whether inference endpoint {id} is in service.
        slots:
          id: path.id
      method: generated
      generated: '2026-10-01'
- target: $.paths['/ml/endpoints/{id}'].delete
  update:
    x-apievangelist-phrasing:
      intent: Delete an ML inference endpoint
      effect: destructive
      questions:
      - Can I tear down an inference endpoint I'm no longer using?
      - Is there a way to remove the endpoint's S3 artifacts too?
      instructions:
      - text: Delete inference endpoint {id}.
        slots:
          id: path.id
      - text: Delete inference endpoint {id} with clean set to {clean}.
        slots:
          id: path.id
          clean: query.clean
      method: generated
      generated: '2026-10-01'