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[第37回 Machine Learning 15minutes!] Microsoft AI - Build 2019 Updates ~ Azure Machine Learning サービスを中心に~

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第37回 Machine Learning 15minutes! (2019/06/29)

https://machine-learning15minutes.connpass.com/event/130683/

[第37回 Machine Learning 15minutes!] Microsoft AI - Build 2019 Updates ~ Azure Machine Learning サービスを中心に~

https://satonaoki.wordpress.com/2019/06/30/ml15-azure-ml-build-2019-updates/

Published in: Software
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[第37回 Machine Learning 15minutes!] Microsoft AI - Build 2019 Updates ~ Azure Machine Learning サービスを中心に~

  1. 1. Microsoft AI: Build 2019 Updates ~ Azure Machine Learning サービスを中心に~ 日本マイクロソフト株式会社 Azureテクノロジスト 佐藤 直生 (@satonaoki)
  2. 2. Azure Machine Learning サービス 機械学習プロセスを効率的に回す仕組みを提供
  3. 3. Azure Machine Learning サービス Azure ML Python SDK ✓Prepare Data ✓Build Models ✓Train Models ✓Manage Models ✓Track Experiments ✓Deploy Models カバー範囲 Azure CLI 機械学習プロジェクトの生産性を向上 企業における機械学習ライフサイクルを管理
  4. 4. Python ライブラリ LightGBM MMLSpark Horovod
  5. 5. プログラム開発環境の制限もなし Azure Notebook Visual Studio Code Jupyter Notebook Databricks Notebook PyCharmNotebook VM
  6. 6. 3つのインタフェースを提供 New Update !
  7. 7. マネージドな計算環境 • 様々なスペックの VM を選択・起動 • 自動スケールアウト/イン • ジョブ管理、スケジュール管理 学習コード train train train ジョブ・スケジュール管理 • 自動でライブラリ/データを準備 ・・・ • 低優先度オプション: 80% 割引で利用可能 Azure Machine Learning コンピューティング
  8. 8. マネージドな推論環境 Azure Container Instances (ACI) Azure Kubernetes Service (AKS) テスト用途 高速スタートアップ 本番用途 自動スケールアウト エッジデバイスで動作 Azure IoT Hub 連携 Azure IoT Edge
  9. 9. ビジュアル インターフェイス
  10. 10. デモ: ビジュアル インターフェイス
  11. 11. 実験の基本フロー 前処理 アルゴリズム選択 ハイパーパラメーター選択 Source: http://scikit- learn.org/stable/tutorial/machine_learning_map/index.html
  12. 12. 自動機械学習 (Automated Machine Learning) Automated ML 最適なモデル生成 データ前処理、モデルアルゴリズム、 ハイパーパラメーターの組合せの中で精度の高いモデルを算 データ セット 制約条件 (処理時間/回数) ゴール設定 (モデル種類/精度評価) 機械学習モデル LightGBM Azure Machine Learning Workspace Scikit-Learn Pipeline形式
  13. 13. あらゆるサービスから Automated ML が利用可能 Automated ML LightGBM
  14. 14. デモ: 自動機械学習 (Automated ML)
  15. 15. MLOps による効率的なライフサイクルの実現 ML コード統合 データ、コードのテスト モデル開発 モデルのパッケージ化 複数環境へデプロイ データ、モデルの監視 モデル再学習・再作成 Infrastructure as Code (IaS) データ準備・加工 (実験的な)モデル開発
  16. 16. Azure DevOps
  17. 17. アプリケーション開発の場合 Model reproducibility Model retrainingModel deploymentModel validation Build appCollaborate Test app Release app Monitor app App developer using Azure DevOps Data scientist using Azure Machine Learning
  18. 18. 機械学習の場合 Model reproducibility Model retrainingModel deploymentModel validation Train model Validate model Deploy model Monitor model Build appCollaborate Test app Release app Monitor app App developer using Azure DevOps Data scientist using Azure Machine Learning Retrain model Azure Machine Learning extension for Azure DevOps Data (Model) Code 自動化パイプラインによって運用管理を効率的に!
  19. 19. デモ: MLOps (Azure Machine Learning + Azure DevOps)
  20. 20. Azure Machine Learning サービス Orchestration Services Monitoring Real-Time Azure Kubernetes Service ML Data Drift Experimentation Monitoring Batch Azure ML Compute Inference Monitoring Compute Azure DevOps ML Extension Storage Model Packaging Model Validation Run History Model Deployment Asset Management Environments Code Datasets ML Audit Trail Training Services Edge Azure IoT Hub
  21. 21. https://azure.microsoft.com/free/ https://azure.microsoft.com/services/machine-learning-service/ https://docs.microsoft.com/azure/machine-learning/service/ui-concept-visual-interface https://docs.microsoft.com/azure/machine-learning/service/concept-automated-ml https://docs.microsoft.com/azure/machine-learning/service/concept-model-management-and- deployment 関連リンク
  22. 22. © 2018 Microsoft Corporation. All rights reserved. 本情報の内容(添付文書、リンク先などを含む)は、作成日時点でのものであり、予告なく変更される場合があります。 © 2019 Microsoft Corporation. All rights reserved. 本情報の内容 (添付文書、リンク先などを含む) は、de:code 2019 開催日 (2019年5月29~30日) 時点のものであり、予告なく変更される場合があります。 本コンテンツの著作権、および本コンテンツ中に出てくる商標権、団体名、ロゴ、製品、サービスなどはそれぞれ、各権利保有者に帰属します。

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