{"created":"2025-01-19T01:29:59.425364+00:00","updated":"2025-01-19T11:12:09.804036+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00230309","sets":["6504:11436:11441"]},"path":["11441"],"owner":"44499","recid":"230309","title":["低費用化により中小規模組織の機械学習活用をめざすハイブリッドMLOps基盤の提案"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"7f75d5ab-929d-4116-b2ab-ab1eb53e7975"},"_deposit":{"id":"230309","pid":{"type":"depid","value":"230309","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"低費用化により中小規模組織の機械学習活用をめざすハイブリッドMLOps基盤の提案","author_link":["620369","620368","620372","620370","620371"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"低費用化により中小規模組織の機械学習活用をめざすハイブリッドMLOps基盤の提案"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ネットワーク","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2023-02-16","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"香川大"},{"subitem_text_value":"香川大"},{"subitem_text_value":"株式会社STNet"},{"subitem_text_value":"香川大"},{"subitem_text_value":"香川大"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/230309/files/IPSJ-Z85-1Y-05.pdf","label":"IPSJ-Z85-1Y-05.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-1Y-05.pdf","filesize":[{"value":"376.7 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"11861c9d-0e0d-4a01-ae85-95254f3f1819","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"平見, 修司"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"竹原, 一駿"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"北, 健志"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"喜田, 弘司"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"亀井, 仁志"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"中小規模程度の組織において機械学習の活用が進められている．例えば，商品の検品に機械学習モデルの利用が検討されている．一方，継続した運用には，精度の劣化を防ぐ再学習が必要であり，運用を開発にフィードバックするMLOpsが適する．MLOps基盤には，機械学習を行う高性能な計算リソースと大容量の学習データを管理するストレージが必要になる．しかし，これら全てをクラウドまたはオンプレミスのいずれか一箇所に用意すると，多くの費用が掛かる．本稿は，MLOps基盤の構成要素をクラウドとオンプレミスに分けて配置することで，再学習とデータ管理，運用を低費用で実現するハイブリッドMLOps基盤を提案する．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"80","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"79","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":230309,"links":{}}