Qiita x Microsoft - 機械学習セミナー Microsoft AI Platform
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Build 2018 の情報も一部取り入れて、データサイエンティスト + Developer のための、Microsoft AI Platform のご紹介です。Data Science VM | Azure Machine Learning Services | Azure IoT Edge までカバーいています。
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Qiita x Microsoft - 機械学習セミナー Microsoft AI Platform
- 4. だから何なの？
- 5. • 雑草 農薬にお金 コンバイン x AI 大幅 応用
- 6. 開発者による AI の採用促進 (API化) ハイブリッドトレーニングとスコアリング シナリオの台頭 事象発生場所でのスコアリング/推論の実施 (エッジ、クラウド、オンプレミス) 一部の開発者が Data Scientist /AI Developer を目指す 多様なハードウェアの進化 (CPU/GPU/FPGA/ASIC/デバイス) データ準備 モデルの展開と管理 モデル系統と監査 モデルについての説明 課 題主 要 動 向
- 7. Bringing the best of AI to Azure and the best of Azure to AI Microsoft AI Platform AI Services AI Infrastructure AI Tools PRE-BUILT AI CONVERSATIONAL AI CUSTOM AI Cognitive Services Bot Framework Azure Machine Learning AI ON DATA AI COMPUTE Data Lake SQL Server Cosmos DB Spark DSVM Batch AI AkS Azure ML Studio Azure ML workbench VS Tools for AI/AML DEEP LEARNING FRAMEWORKS Cognitive Toolkit TensorFlow Chainer Others (Azure Notebooks, Pycharm…) Others (Scikit-learn, Keras, PyTorch, MxNet, Caffe…) CODING AND MANAGEMENT TOOLS IoT AI SILICON
- 11. Client Azure Batch Docker Repository ・コマンド - ジョブの開始など ・設定ファイル - アカウント情報 - Docker イメージ名 - 実行コマンド など Docker Image
- 13. F F F L0 L1 F F F L0 Pretrained DNN Model in TensorFlow, CNTK, etc. Scalable DNN Hardware Microservice BrainWave Soft DPU Instr Decoder & Control Neural FU A Scalable FPGA-Powered DNN Serving Platform Fast: Flexible Friendly: Network switches FPGAs
- 14. Azure ML integration End-to-end deployment and model lifecycle support Hardware Accelerated Model Gallery Brainwave Compiler & Runtime “Brainslice” Soft Neural Processing Unit
- 15. Model Management Service Azure ML orchestratorPython and TensorFlow Featurize images and train classifier Classifier (TF/LGBM) Preprocessing (TensorFlow, C++ API) Control Plane Service Brain Wave Runtime FPGA CPU
- 17. Custom AI
- 20. Azure Machine Learning Experimentation Service Azure Machine Learning Model Management Service 推論 デプロイメントデータの準備 モデル構築・学習
- 21. Spark SQL Server GPU インスタンス コンテナー サービス Azure Machine Learning Workbench / AI Tools for VS SQL Server Machine Learning Server オンプレミス エッジ コンピュー ティング Azure IoT Edge 実験および モデル管理 Azure Machine Learning Service トレーニングとデプロ イ Azure
- 23. Azure IoT Edge IoT Hub Devices Local Storage Azure Machine Learning (Container) Module Management Agent Device Twin Device Twin Azure Stream Analytics (Container) Azure Functions (Container) Cognitive Services (Container) Custom Code (Container) Module Twin Module Twin Module Twin Module Twin Module Twin Module Twin Module Twin Module Twin Module Twin Module TwinModule Twin Module Twin Module Twin Module Twin Device Twin Module Twin Module Twin Module Twin Module Twin Device Twin IoT Hub
- 24. <Python> Modelの保存 出力された Model <C#>Model の読み込み <C#>Model の利用 (実行)
- 25. ApplicationAdd Model Reference Model Windows ML Training Environment 1. Load – Loads model into Windows ML runtime 2. Bind – Wires up inputs and outputs to model 3. Eval – Evaluates the model and products results
- 26. Direct3D GPU VPU NPU CPU DirectML API Model Inference Engine WinML COM API WinML RT API Input Surface Output Surface Application #1 WinML Runtime Application #2
- 27. Pre-built AI
- 32. #azurejp
- 33. #azurejp
- 34. © 2018 Microsoft Corporation. All rights reserved. Microsoft, Windows and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.