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Amazon Machine Learning Tutorial

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Amazon Machine Learning Tutorial @ Tech-Circle #5 Machine Learning Bootstrap

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Amazon Machine Learning Tutorial

  1. 1. 2015/4/27 Tech-Circle#5 @tominaga443 Amazon Machine Learning Tutorial
  2. 2. Introduce  冨永 善視 (とみなが よしみ)  Twitter @tominaga443  業務  クラウド関連のR&D  http://cloudconductor.org/  OpenStack, Docker, Consul
  3. 3. 鋭意勉強中ですのでお手柔らかにお願いします
  4. 4. Public Cloud Services
  5. 5. Machine Learning as a Service
  6. 6. Amazon Machine Learning
  7. 7. Amazon Machine Learning  Amazon社内で使用されてきたテクノロジー  2015/4/9 サービス開始  現在はUS East(N. Virginia)リージョンのみ  データはS3, Redshift, RDS(MySQL)から投入  CloudWatchで予測実行件数をモニタリング
  8. 8. Available Tasks  binary classification(二項分類) – 手法:ロジスティック回帰 – 用途:メールのフィルタリング  multiclass classification(多クラス分類) – 手法:多クラスロジスティック回帰 – 用途:映画のジャンル判定  regression(数値予測) – 手法:線形回帰 – 用途:商品の売上予測
  9. 9. Key Concepts  Datasources – 学習・評価に使うデータ  ML Models – 学習データを使って作成したモデル  Evaluations – モデルの評価結果  Predictions – 本番データの予測結果
  10. 10. Tutorial  サンプルデータ – 金融機関の定期預金キャンペーンに関するデータ – 顧客情報、経済指標など – 顧客が定期預金を契約するかしないかを予測  手順 – データの投入 – 学習データと評価データに分ける – モデルの作成 – モデルの評価
  11. 11. Demo  Tutorial手順 – http://docs.aws.amazon.com/machine- learning/latest/dg/tutorial.html  AWSにログインしてAmazon Machine Learningを選択  Create new Datasource & ML Modelを選択  DatasourceはS3のサンプルデータのURLを入力  Target(予測対象)は21列目の y を指定  後はガイドに従って進んでいけばモデルを作成できる
  12. 12. Demo  Datasourceとモデルが作成される
  13. 13. Demo  モデルの評価結果
  14. 14. Demo  閾値チューニング後のモデル
  15. 15. Conclusion  機械学習を簡単に試してみたい人にはおすすめ  モデルの閾値を簡単に調整できる  機械学習サービスとしてはまだまだ物足りない – 学習アルゴリズム – 前処理機能 – 実行速度
  16. 16. More Information  今回の内容をQiitaにまとめました – Amazon Machine Learningのサンプルをちょっとチューニ ングしてみる – http://qiita.com/tominaga443/items/7c91ab873b25784 45614  ご興味がある方はぜひご覧ください
  17. 17. THANK YOU

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