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Kartu SageMaker Model Amazon
penting
Kartu SageMaker Model Amazon terintegrasi dengan SageMaker Model Registry. Jika Anda mendaftarkan model dalam Model Registry, Anda dapat menggunakan integrasi untuk menambahkan informasi audit. Untuk informasi selengkapnya, lihat Lihat dan Perbarui Detail Versi Model.
Gunakan Kartu SageMaker Model Amazon untuk mendokumentasikan detail penting tentang model pembelajaran mesin (ML) Anda di satu tempat untuk tata kelola dan pelaporan yang efisien.
Detail katalog seperti tujuan penggunaan dan peringkat risiko model, detail dan metrik pelatihan, hasil evaluasi dan pengamatan, dan panggilan tambahan seperti pertimbangan, rekomendasi, dan informasi khusus. Dengan membuat kartu model, Anda dapat melakukan hal berikut:
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Memberikan panduan tentang bagaimana model harus digunakan.
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Support kegiatan audit dengan deskripsi rinci tentang pelatihan model dan kinerja.
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Komunikasikan bagaimana model dimaksudkan untuk mendukung tujuan bisnis.
Kartu model memberikan panduan preskriptif tentang informasi apa yang akan didokumentasikan dan menyertakan bidang untuk informasi khusus. Setelah membuat kartu model, Anda dapat mengekspornya ke PDF atau mengunduhnya untuk dibagikan dengan pemangku kepentingan yang relevan. Setiap pengeditan selain pembaruan status persetujuan yang dilakukan pada kartu model menghasilkan versi kartu model tambahan agar memiliki catatan perubahan model yang tidak dapat diubah.
Topik
Prasyarat
Untuk memulai dengan Kartu SageMaker Model Amazon, Anda harus memiliki izin untuk membuat, mengedit, melihat, dan mengekspor kartu model.
Penggunaan model yang dimaksudkan
Menentukan tujuan penggunaan model membantu memastikan bahwa pengembang model dan pengguna memiliki informasi yang mereka butuhkan untuk melatih atau menyebarkan model secara bertanggung jawab. Penggunaan model yang dimaksudkan harus menggambarkan skenario di mana model sesuai untuk digunakan serta skenario di mana model tidak disarankan untuk digunakan.
Kami merekomendasikan termasuk:
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Tujuan umum dari model
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Gunakan kasus yang modelnya dimaksudkan
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Gunakan kasus yang modelnya tidak dimaksudkan
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Asumsi yang dibuat saat mengembangkan model
Penggunaan model yang dimaksudkan melampaui rincian teknis dan menjelaskan bagaimana model harus digunakan dalam produksi, skenario di mana sesuai untuk menggunakan model, dan pertimbangan tambahan seperti jenis data yang akan digunakan dengan model atau asumsi apa pun yang dibuat selama pengembangan.
Peringkat risiko
Pengembang membuat model ML untuk kasus penggunaan dengan berbagai tingkat risiko. Misalnya, model yang menyetujui aplikasi pinjaman mungkin merupakan model risiko yang lebih tinggi daripada model yang mendeteksi kategori email. Mengingat profil risiko yang bervariasi dari suatu model, kartu model menyediakan bidang bagi Anda untuk mengkategorikan peringkat risiko model.
Peringkat risiko ini bisaunknown,low,medium, atauhigh. Gunakan bidang peringkat risiko ini untuk memberi label model yang tidak diketahui, rendah, sedang, atau berisiko tinggi dan bantu organisasi Anda mematuhi aturan yang ada tentang memasukkan model tertentu ke dalam produksi.
JSONSkema kartu model
Rincian evaluasi untuk kartu model harus disediakan dalam JSON format. Jika Anda memiliki laporan evaluasi JSON format yang dibuat oleh SageMaker Clarify atau SageMaker Model Monitor, unggah laporan tersebut ke Amazon S3 dan berikan S3 URI untuk mengurai metrik evaluasi secara otomatis. Untuk informasi selengkapnya dan contoh laporan, lihat folder metrik contoh
Saat membuat kartu model menggunakan SageMaker PythonSDK, konten model harus dalam JSON skema kartu model dan disediakan sebagai string. Berikan konten model yang mirip dengan contoh berikut.
{ "$schema": "http://json-schema.org/draft-07/schema#", "$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 } } }
