watsonx.ai kinds

The watsonx_ai service has 7 resource kinds, each listed below with its endpoints, fields, and dependencies. Values resolve late, so you reference another resource with ${kind.ref_name}. For defaults, validation rules, and examples, run wxctl explain <kind>.

YAML envelope & reference syntax

• Envelope: A config is one or more YAML documents separated by ---. Each has top-level kind and ref_name, then the fields below at the top level (not nested under spec).
• ref_name: Unique handle for this resource within the config. Used to reference it from other resources, then stripped before the API call (not a schema field).
• References: Reference another resource by its ref_name: ${<kind>.<ref_name>} resolves to its id, ${<kind>.<ref_name>.<field>} to a specific field. Values resolve late, at plan/apply time.

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ai_service

An AI Service is a deployable code artifact for Watson Machine Learning. Runs on SaaS + Software · ID field id Endpoints Fields Computed outputs: id, created_at. Depends on

• software_spec → software_specification (required)
• space_id → space (optional)
• project_id → project (optional)

Consumers

• wml_deployment → .asset (required)

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autoai_experiment

An AutoAI Experiment runs a watsonx.ai AutoAI training to completion and surfaces the ranked pipeline leaderboard. Runs on SaaS + Software · ID field id Endpoints Fields Computed outputs: id, pipeline_id, state, best_pipeline, leaderboard. Depends on

• training_data → data_asset (required)
• space_id → space (optional)
• project_id → project (optional)

Consumers

• wml_model → .experiment (required)

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notebook

A Notebook asset stored in a watsonx.ai project. Runs on SaaS + Software · ID field guid Endpoints Fields Computed outputs: guid, created_at. Depends on

• environment → environment (optional)
• project_id → project (optional)

Consumers

• job → .asset (optional)

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wml_deployment

A WML Deployment creates a live online endpoint from an AI service or model. Runs on SaaS + Software · ID field id Endpoints Fields Computed outputs: id, created_at, status, scoring_url. Depends on

• asset → ai_service (required)
• asset → wml_function (optional)
• asset → wml_script (optional)
• asset → wml_model (optional)
• asset → asset_promotion (optional)
• space_id → space (optional)
• project_id → project (optional)

Consumers

• model → .provider_config.watsonx_deployment_id (optional)
• subscription → .asset.asset_id (required)
• subscription → .deployment.deployment_id (required)

Advisories

• warn (doc_cited, 2026-06-12): A watsonx.ai online deployment (wml_deployment) is scoped to a deployment space, not a project. Using a common_core_project as the deployment container does not work: the deployment, its software specification, and its function all expect a space id, and a project reference cannot supply one. Create the deployable asset and its deployment inside a common_core_space.

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wml_function

A Python function asset stored in Watson Machine Learning. Runs on SaaS + Software · ID field id Endpoints Fields Computed outputs: id, created_at. Depends on

• software_spec → software_specification (required)
• space_id → space (optional)
• project_id → project (optional)

Consumers

• model_tracking → .model (optional)
• wml_deployment → .asset (optional)

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wml_model

A WML Model materializes the winning pipeline of a completed AutoAI experiment as a deployable wml-hybrid_0.1 model. Runs on SaaS + Software · ID field id Endpoints Fields Computed outputs: id, model_type, pipeline_node, created_at. Depends on

• experiment → autoai_experiment (required)
• space_id → space (optional)
• project_id → project (optional)

Consumers

• model_tracking → .model (required)
• wml_deployment → .asset (optional)

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wml_script

A Python script asset stored in Watson Machine Learning. Runs on SaaS + Software · ID field id Endpoints Fields Computed outputs: id, created_at. Depends on

• software_spec → software_specification (required)
• space_id → space (optional)
• project_id → project (optional)

Consumers

• wml_deployment → .asset (optional)