Introduction to Model Registry

「モデルのバージョン管理どうするのがいいですか」と聞かれることが多くなってきたので作成した資料です。

Transcript

1. Introduction to Model Registry
   

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13. import json
    import model_card_toolkit as mctlib
    model_card_json = {
    'model_details': {'name': 'Census Income Classifier'},
    'model_details': {'overview': 'This is a wide and deep Keras model...'},
    'model_details': {
    'owners':[{"name": "Model Cards Team", "contact": "model-cards@google.com"}]},
    'considerations':{
    'use_cases':[{
    "description":'This dataset that this model was trained on...'}]
    },
    'considerations': {
    'limitations': [{
    'description':'This is a class-imbalanced dataset...'}]
    },
    'considerations': {
    'ethical_considerations': [{
    'name': 'We risk expressing the viewpoint that...',
    'mitigation_strategy': 'As mentioned, some interventions may need to be ...'
    }]
    }
    }
    

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21. # Specify the model source
    model_url = "s3://your-bucket-name/model.tar.gz"
    modelpackage_inference_specification = {
    "InferenceSpecification": {
    "Containers": [
    {
    "Image": '257758044811.dkr.ecr.us-east-2.amazonaws.com/sagemaker-xgboost:1.2-1',
    "ModelDataUrl": model_url
    }
    ],
    "SupportedContentTypes": [ "text/csv" ],
    "SupportedResponseMIMETypes": [ "text/csv" ],
    }
    }
    

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22. create_model_package_input_dict = {
    "ModelPackageGroupName" : model_package_group_name,
    "ModelPackageDescription" :
    "Model to detect 3 different types of irises (Setosa, Versicolour, and Virginica)",
    "ModelApprovalStatus" : "PendingManualApproval"
    }
    create_model_package_input_dict.update(modelpackage_inference_specification)
    

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