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
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 ...' }] } }
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" ], } }
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)