Las traducciones son generadas a través de traducción automática. En caso de conflicto entre la traducción y la version original de inglés, prevalecerá la version en inglés.
Tarjetas SageMaker modelo Amazon
importante
Amazon SageMaker Model Card está integrada con SageMaker Model Registry. Si va a registrar un modelo en Model Registry, puede utilizar la integración para añadir información de auditoría. Para obtener más información, consulte Vea y actualice los detalles de una versión de modelo.
Utilice Amazon SageMaker Model Cards para documentar detalles importantes sobre sus modelos de aprendizaje automático (ML) en un solo lugar para agilizar la gobernanza y la elaboración de informes.
Catalogue detalles, como el uso previsto y la clasificación de riesgo de un modelo, los detalles y las métricas del entrenamiento, los resultados y las observaciones de la evaluación, y elementos adicionales, como consideraciones, recomendaciones e información personalizada. Al crear tarjetas de modelos, puede hacer lo siguiente:
-
Proporcionar orientación sobre cómo debe usarse un modelo.
-
Respaldar las actividades de auditoría con descripciones detalladas del entrenamiento y rendimiento del modelo.
-
Comunicar cómo se pretende que un modelo respalde los objetivos empresariales.
Las tarjetas de modelos proporcionan una guía prescriptiva sobre la información que se debe documentar e incluyen campos para información personalizada. Tras crear una tarjeta modelo, puede exportarla a una PDF o descargarla para compartirla con las partes interesadas pertinentes. Cualquier modificación, salvo la actualización del estado de aprobación, realizada en una tarjeta de modelo da como resultado versiones adicionales de la tarjeta de modelo para tener un registro inmutable de los cambios del modelo.
Temas
- Requisitos previos
- Usos previstos de un modelo
- Clasificaciones de riesgo
- JSONEsquema de tarjetas modelo
- Para crear una tarjeta de modelo
- Administración de las tarjetas de modelos
- Soporte multicuenta para Amazon SageMaker Model Cards
- Utilice modelos de tarjetas en los niveles más bajos APIs
- Modelo de tarjeta FAQs
Requisitos previos
Para empezar a utilizar las tarjetas SageMaker modelo de Amazon, debes tener permiso para crear, editar, ver y exportar modelos de tarjetas.
Usos previstos de un modelo
Especificar los usos previstos de un modelo ayuda a garantizar que los desarrolladores y los usuarios del modelo dispongan de la información que necesitan para entrenar o implementar el modelo de forma responsable. Los usos previstos de un modelo deben describir los escenarios en los que es apropiado usarlo, así como los escenarios en los que no se recomienda su uso.
Se recomienda incluir:
-
El propósito general del modelo
-
Casos de uso para los que se diseñó el modelo
-
Casos de uso para los que no se diseñó el modelo
-
Suposiciones hechas al desarrollar el modelo
Los usos previstos de un modelo van más allá de los detalles técnicos y describen cómo se debe utilizar un modelo en producción, los escenarios en los que es apropiado utilizar un modelo y otras consideraciones, como el tipo de datos que se van a utilizar con el modelo o cualquier hipótesis que se haya hecho durante el desarrollo.
Clasificaciones de riesgo
Los desarrolladores crean modelos de ML para casos de uso con distintos niveles de riesgo. Por ejemplo, un modelo que apruebe solicitudes de préstamo puede ser un modelo de mayor riesgo que uno que detecte la categoría de un correo electrónico. Dados los diversos perfiles de riesgo de un modelo, las tarjetas de modelos proporcionan un campo para categorizar la clasificación de riesgo de un modelo.
Esta clasificación de riesgo puede ser unknown, low, medium o high. Utilice estos campos de clasificación de riesgo para etiquetar modelos desconocidos, de riesgo bajo, medio o alto y ayudar a su organización a cumplir con las normas vigentes sobre la puesta en producción de determinados modelos.
JSONEsquema de tarjetas modelo
Los detalles de evaluación de un modelo de tarjeta deben proporcionarse en JSON formato. Si ya tiene informes de evaluación de JSON formato generados por SageMaker Clarify o SageMaker Model Monitor, cárguelos en Amazon S3 y proporcione un S3 URI para analizar automáticamente las métricas de evaluación. Para obtener más información y ejemplos de informes, consulta la carpeta de métricas de ejemplo
Al crear una tarjeta modelo con SageMaker PythonSDK, el contenido del modelo debe estar en el JSON esquema de la tarjeta modelo y debe proporcionarse como una cadena. Proporcione un contenido del modelo similar al del siguiente ejemplo.
{ "$schema": "http://json-schema.org/draft-07/schema#", "$id": "http://json-schema.org/draft-07/schema#", "title": "SageMakerModelCardSchema", "description": "Default model card schema", "version": "0.1.0", "type": "object", "additionalProperties": false, "properties": { "model_overview": { "description": "Overview about the model", "type": "object", "additionalProperties": false, "properties": { "model_description": { "description": "description of model", "type": "string", "maxLength": 1024 }, "model_owner": { "description": "Owner of model", "type": "string", "maxLength": 1024 }, "model_creator": { "description": "Creator of model", "type": "string", "maxLength": 1024 }, "problem_type": { "description": "Problem being solved with the model", "type": "string" }, "algorithm_type": { "description": "Algorithm used to solve the problem", "type": "string", "maxLength": 1024 }, "problem_type": { "description": "Problem being solved with the model", "type": "string" }, "model_owner": { "description": "Owner of model", "type": "string", "maxLength": 1024 } }, "model_id": { "description": "SageMaker Model Arn or Non SageMaker Model id", "type": "string", "maxLength": 1024 }, "model_artifact": { "description": "Location of the model artifact", "type": "array", "maxContains": 15, "items": { "type": "string", "maxLength": 1024 } }, "model_name": { "description": "Name of the model", "type": "string", "maxLength": 1024 }, "model_version": { "description": "Version of the model", "type": "number", "minimum": 1 }, "inference_environment": { "description": "Overview about the inference", "type": "object", "additionalProperties": false, "properties": { "container_image": { "description": "SageMaker inference image uri", "type": "array", "maxContains": 15, "items": { "type": "string", "maxLength": 1024 } } } } } }, "model_package_details": { "description": "Metadata information related to model package version", "type": "object", "additionalProperties": false, "properties": { "model_package_description": { "description": "A brief summary of the model package", "type": "string", "maxLength": 1024 }, "model_package_arn": { "description": "The Amazon Resource Name (ARN) of the model package", "type": "string", "minLength": 1, "maxLength": 2048 }, "created_by": { "description": "Information about the user who created model package.", "type": "object", "additionalProperties": false, "properties": { "user_profile_name": { "description": "The name of the user's profile in SageMaker Studio", "type": "string", "maxLength": 63 } } }, "model_package_status": { "description": "Current status of model package", "type": "string", "enum": [ "Pending", "InProgress", "Completed", "Failed", "Deleting" ] }, "model_approval_status": { "description": "Current approval status of model package", "type": "string", "enum": [ "Approved", "Rejected", "PendingManualApproval" ] }, "approval_description": { "description": "A description provided for the model approval", "type": "string", "maxLength": 1024 }, "model_package_group_name": { "description": "If the model is a versioned model, the name of the model group that the versioned model belongs to.", "type": "string", "minLength": 1, "maxLength": 63 }, "model_package_name": { "description": "Name of the model package", "type": "string", "minLength": 1, "maxLength": 63 }, "model_package_version": { "description": "Version of the model package", "type": "number", "minimum": 1 }, "domain": { "description": "The machine learning domain of the model package you specified. Common machine learning domains include computer vision and natural language processing.", "type": "string" }, "task": { "description": "The machine learning task you specified that your model package accomplishes. Common machine learning tasks include object detection and image classification.", "type": "string" }, "source_algorithms": { "description": "A list of algorithms that were used to create a model package.", "$ref": "#/definitions/source_algorithms" }, "inference_specification": { "description": "Details about inference jobs that can be run with models based on this model package.", "$ref": "#/definitions/inference_specification" } } }, "intended_uses": { "description": "Intended usage of model", "type": "object", "additionalProperties": false, "properties": { "purpose_of_model": { "description": "Why the model was developed?", "type": "string", "maxLength": 2048 }, "intended_uses": { "description": "intended use cases", "type": "string", "maxLength": 2048 }, "factors_affecting_model_efficiency": { "type": "string", "maxLength": 2048 }, "risk_rating": { "description": "Risk rating for model card", "$ref": "#/definitions/risk_rating" }, "explanations_for_risk_rating": { "type": "string", "maxLength": 2048 } } }, "business_details": { "description": "Business details of model", "type": "object", "additionalProperties": false, "properties": { "business_problem": { "description": "What business problem does the model solve?", "type": "string", "maxLength": 2048 }, "business_stakeholders": { "description": "Business stakeholders", "type": "string", "maxLength": 2048 }, "line_of_business": { "type": "string", "maxLength": 2048 } } }, "training_details": { "description": "Overview about the training", "type": "object", "additionalProperties": false, "properties": { "objective_function": { "description": "the objective function the model will optimize for", "function": { "$ref": "#/definitions/objective_function" }, "notes": { "type": "string", "maxLength": 1024 } }, "training_observations": { "type": "string", "maxLength": 1024 }, "training_job_details": { "type": "object", "additionalProperties": false, "properties": { "training_arn": { "description": "SageMaker Training job arn", "type": "string", "maxLength": 1024 }, "training_datasets": { "description": "Location of the model datasets", "type": "array", "maxContains": 15, "items": { "type": "string", "maxLength": 1024 } }, "training_environment": { "type": "object", "additionalProperties": false, "properties": { "container_image": { "description": "SageMaker training image uri", "type": "array", "maxContains": 15, "items": { "type": "string", "maxLength": 1024 } } } }, "training_metrics": { "type": "array", "items": { "maxItems": 50, "$ref": "#/definitions/training_metric" } }, "user_provided_training_metrics": { "type": "array", "items": { "maxItems": 50, "$ref": "#/definitions/training_metric" } }, "hyper_parameters": { "type": "array", "items": { "maxItems": 100, "$ref": "#/definitions/training_hyper_parameter" } }, "user_provided_hyper_parameters": { "type": "array", "items": { "maxItems": 100, "$ref": "#/definitions/training_hyper_parameter" } } } } } }, "evaluation_details": { "type": "array", "default": [], "items": { "type": "object", "required": [ "name" ], "additionalProperties": false, "properties": { "name": { "type": "string", "pattern": ".{1,63}" }, "evaluation_observation": { "type": "string", "maxLength": 2096 }, "evaluation_job_arn": { "type": "string", "maxLength": 256 }, "datasets": { "type": "array", "items": { "type": "string", "maxLength": 1024 }, "maxItems": 10 }, "metadata": { "description": "additional attributes associated with the evaluation results", "type": "object", "additionalProperties": { "type": "string", "maxLength": 1024 } }, "metric_groups": { "type": "array", "default": [], "items": { "type": "object", "required": [ "name", "metric_data" ], "properties": { "name": { "type": "string", "pattern": ".{1,63}" }, "metric_data": { "type": "array", "items": { "anyOf": [ { "$ref": "#/definitions/simple_metric" }, { "$ref": "#/definitions/linear_graph_metric" }, { "$ref": "#/definitions/bar_chart_metric" }, { "$ref": "#/definitions/matrix_metric" } ] } } } } } } } }, "additional_information": { "additionalProperties": false, "type": "object", "properties": { "ethical_considerations": { "description": "Any ethical considerations that the author wants to provide", "type": "string", "maxLength": 2048 }, "caveats_and_recommendations": { "description": "Caveats and recommendations for people who might use this model in their applications.", "type": "string", "maxLength": 2048 }, "custom_details": { "type": "object", "additionalProperties": { "$ref": "#/definitions/custom_property" } } } } }, "definitions": { "source_algorithms": { "type": "array", "minContains": 1, "maxContains": 1, "items": { "type": "object", "additionalProperties": false, "required": [ "algorithm_name" ], "properties": { "algorithm_name": { "description": "The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in AWS Marketplace that you are subscribed to.", "type": "string", "maxLength": 170 }, "model_data_url": { "description": "The Amazon S3 path where the model artifacts, which result from model training, are stored.", "type": "string", "maxLength": 1024 } } } }, "inference_specification": { "type": "object", "additionalProperties": false, "required": [ "containers" ], "properties": { "containers": { "description": "Contains inference related information which were used to create model package.", "type": "array", "minContains": 1, "maxContains": 15, "items": { "type": "object", "additionalProperties": false, "required": [ "image" ], "properties": { "model_data_url": { "description": "The Amazon S3 path where the model artifacts, which result from model training, are stored.", "type": "string", "maxLength": 1024 }, "image": { "description": "Inference environment path. The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.", "type": "string", "maxLength": 255 }, "nearest_model_name": { "description": "The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model.", "type": "string" } } } } } }, "risk_rating": { "description": "Risk rating of model", "type": "string", "enum": [ "High", "Medium", "Low", "Unknown" ] }, "custom_property": { "description": "Additional property in section", "type": "string", "maxLength": 1024 }, "objective_function": { "description": "objective function that training job is optimized for", "additionalProperties": false, "properties": { "function": { "type": "string", "enum": [ "Maximize", "Minimize" ] }, "facet": { "type": "string", "maxLength": 63 }, "condition": { "type": "string", "maxLength": 63 } } }, "training_metric": { "description": "training metric data", "type": "object", "required": [ "name", "value" ], "additionalProperties": false, "properties": { "name": { "type": "string", "pattern": ".{1,255}" }, "notes": { "type": "string", "maxLength": 1024 }, "value": { "type": "number" } } }, "training_hyper_parameter": { "description": "training hyper parameter", "type": "object", "required": [ "name", "value" ], "additionalProperties": false, "properties": { "name": { "type": "string", "pattern": ".{1,255}" }, "value": { "type": "string", "pattern": ".{1,255}" } } }, "linear_graph_metric": { "type": "object", "required": [ "name", "type", "value" ], "additionalProperties": false, "properties": { "name": { "type": "string", "pattern": ".{1,255}" }, "notes": { "type": "string", "maxLength": 1024 }, "type": { "type": "string", "enum": [ "linear_graph" ] }, "value": { "anyOf": [ { "type": "array", "items": { "type": "array", "items": { "type": "number" }, "minItems": 2, "maxItems": 2 }, "minItems": 1 } ] }, "x_axis_name": { "$ref": "#/definitions/axis_name_string" }, "y_axis_name": { "$ref": "#/definitions/axis_name_string" } } }, "bar_chart_metric": { "type": "object", "required": [ "name", "type", "value" ], "additionalProperties": false, "properties": { "name": { "type": "string", "pattern": ".{1,255}" }, "notes": { "type": "string", "maxLength": 1024 }, "type": { "type": "string", "enum": [ "bar_chart" ] }, "value": { "anyOf": [ { "type": "array", "items": { "type": "number" }, "minItems": 1 } ] }, "x_axis_name": { "$ref": "#/definitions/axis_name_array" }, "y_axis_name": { "$ref": "#/definitions/axis_name_string" } } }, "matrix_metric": { "type": "object", "required": [ "name", "type", "value" ], "additionalProperties": false, "properties": { "name": { "type": "string", "pattern": ".{1,255}" }, "notes": { "type": "string", "maxLength": 1024 }, "type": { "type": "string", "enum": [ "matrix" ] }, "value": { "anyOf": [ { "type": "array", "items": { "type": "array", "items": { "type": "number" }, "minItems": 1, "maxItems": 20 }, "minItems": 1, "maxItems": 20 } ] }, "x_axis_name": { "$ref": "#/definitions/axis_name_array" }, "y_axis_name": { "$ref": "#/definitions/axis_name_array" } } }, "simple_metric": { "description": "metric data", "type": "object", "required": [ "name", "type", "value" ], "additionalProperties": false, "properties": { "name": { "type": "string", "pattern": ".{1,255}" }, "notes": { "type": "string", "maxLength": 1024 }, "type": { "type": "string", "enum": [ "number", "string", "boolean" ] }, "value": { "anyOf": [ { "type": "number" }, { "type": "string", "maxLength": 63 }, { "type": "boolean" } ] }, "x_axis_name": { "$ref": "#/definitions/axis_name_string" }, "y_axis_name": { "$ref": "#/definitions/axis_name_string" } } }, "axis_name_array": { "type": "array", "items": { "type": "string", "maxLength": 63 } }, "axis_name_string": { "type": "string", "maxLength": 63 } } }
