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MOV の機械学習システムを支える MLOps 実践【DeNA TechCon 2020 ライブ配信】

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本セッションでは機械学習システム特有の課題を踏まえ、『お客さま探索ナビ』の開発・運用における MLOps の実践について、実際の取り組みとともに紹介します。
具体的には、MLOps 技術の選定理由とナレッジ、MLOps のハマりポイントと解決策、さらなる自動化に向けて現在取り組んでいる内容などをお話し
します。

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MOV の機械学習システムを支える MLOps 実践【DeNA TechCon 2020 ライブ配信】

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  41. 41. DeNA TechCon 2020 #denatechcon 々 Map data ©2019 Google
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  44. 44. DeNA TechCon 2020 #denatechcon 々 AI Platform TrainingBigQuery
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  54. 54. DeNA TechCon 2020 #denatechcon def task_fail_slack_alert(context): slack_msg = “Task Failed.” failed_alert = SlackWebhookOperator( task_id="xxx", http_conn_id="slack", webhook_token=Variable.get("webhook.token"), message=slack_msg, username="airflow", ) return failed_alert.execute(context=context)
  55. 55. DeNA TechCon 2020 #denatechcon <!subteam^ABCDEF123|@user_group>
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  69. 69. DeNA TechCon 2020 #denatechcon ※ @pytest.fixture(scope="session", autouse=True) def setup_teardown(request, test_config): # setup # suffix = uuid.uuid4() create_dataset(suffix) yield # teardown # delete_blobs(suffix) delete_dataset(suffix)
  70. 70. DeNA TechCon 2020 #denatechcon @pytest.mark.dependency(depends=[“test_preprocess_a”, “test_preprocess_b”]) def test_trainer(test_config): trainer = Trainer( test_config[“xxx”].format(test_config[“hash"]), test_config[“yyy”].format(test_config[“hash"]), ) trainer.train() trainer.upload_model()
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