Azure mltocustomyourai_dotnetconf2019tw
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.Net Conference會場上分享Azure ML 服務的投影片
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Azure mltocustomyourai_dotnetconf2019tw
- 1. 探索 .NET 新世界 Host by
- 3. 陳潔寧 Ning Data For Social Good 顧問 Data For Social Good 專任顧問，輔導過社會及環保相 關議題的資料分析案， R-Ladies Taipei Co-Founder 2017、2018、2019年在AI類別取得微軟MVP殊榮，活 耀於AI、機器學習相關研究、社群、競賽活動並且多場 海內外AI相關專題演講經驗。帶領AI團隊與台北市社會 救助科拿到聯發科「智在家鄉」百萬首獎。
- 6. Extract intent Detect empty shelvesExtract identity frameworks TensorFlow KerasPytorch Onnx ML Pre-built, customizable services face OCR text vision speech translation QnA ...... use case featuresF1 F2 F3 F4 how deep in the stack?
- 7. frameworks TensorFlow KerasPytorch Onnx ML Pre-built, customizable services face OCR text vision speech translation QnA ...... effort pioneer expert developer everyone generic specialized technology
- 9. Machine Learning on Azure Domain specific pretrained models To simplify solution development Popular frameworks To build advanced deep learning solutions Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Familiar data science tools To simplify model development From the Intelligent Cloud to the Intelligent Edge Azure Databricks Machine Learning VMs TensorFlowPyTorch ONNX LanguageSpeech … DecisionVision Scikit-Learn Azure Notebooks JupyterVisual Studio Code Command line Azure Machine Learning CPU GPU FPGA Web search
- 19. …
- 27. End to end lifecycle managementSimplified machine learning Open platform
- 28. End to end lifecycle managementSimplified machine learning Open platform
- 29. Automated machine learning UI Visual interface Machine learning notebooks New capabilities in Azure Machine Learning service
- 30. Visual interface Machine learning notebooks Automated machine learning UI New capabilities in Azure Machine Learning service
- 31. Automated machine learning UI Machine learning notebooks Visual interface New capabilities in Azure Machine Learning service
- 32. Automated machine learning UI Visual interface Machine learning notebooks New capabilities in Azure Machine Learning service
- 33. End to end lifecycle managementSimplified machine learning Open platform
- 34. DevOps MLOps Code testing Code reproducibility App deployment Model retraining Model validation Model reproducibility Model deployment
- 35. App developer using Azure DevOps MLOps with Azure Machine Learning Build appCollaborate Test app Release app Monitor app Model reproducibility Model retrainingModel deploymentModel validation Data scientist using Azure Machine Learning
- 36. MLOps with Azure Machine Learning Code, dataset, and environment versioning 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
- 37. MLOps with Azure Machine Learning Model validation & profiling Model reproducibility Model retrainingModel deploymentModel validation Train model Validate model Build appCollaborate Test app Release app Monitor app App developer using Azure DevOps Data scientist using Azure Machine Learning
- 38. MLOps with Azure Machine Learning Model packaging Simple deployment across cloud and edge Model reproducibility Model retrainingModel deploymentModel validation Train model Validate model Deploy model Build appCollaborate Test app Release app Monitor app App developer using Azure DevOps Data scientist using Azure Machine Learning
- 39. MLOps with Azure Machine Learning Model management & monitoring Model performance analysis Model reproducibility Model retrainingModel deploymentModel validation Train model Validate model Deploy model Monitor model Retrain model Build appCollaborate Test app Release app Monitor app App developer using Azure DevOps Data scientist using Azure Machine Learning
- 40. MLOps with Azure Machine Learning Train model Validate model Deploy model Monitor model Retrain model Model reproducibility Model retrainingModel deploymentModel validation Build appCollaborate Test app Release app Monitor app Azure Machine Learning extension for Azure DevOps App developer using Azure DevOps Data scientist using Azure Machine Learning
- 41. MLOps with Azure Machine Learning Model reproducibility Model retrainingModel deploymentModel validation Train model Validate model Deploy model Monitor model Retrain model Build appCollaborate Test app Release app Monitor app Audit trail management and model interpretability App developer using Azure DevOps Data scientist using Azure Machine Learning
- 49. Thanks
- 50. 特別感謝
Editor's Notes
- https://youtu.be/lhMu94uCzR0?t=185 你可以認出鳥在哪裡、鳥的種類、鳥的足跡 從原本要五年的學位到現在很快就可以即時偵測
- ML frameworks require ML experts, CogS are prebuilt, specialized pieces of AI which are meant for developers to use. If a problem can be addressed at a higher level, it will be cheaper to address.
- ML frameworks require ML experts, CogS are prebuilt, specialized pieces of AI which are meant for developers to use. If a problem can be addressed at a higher level, it will be cheaper to address.
- 團隊共同使用 workspace(工作區) Compute – 使用skilearn、要做影像運算、 Experiments 使用code Data sore – centralized data location Models – 很快調整第一版 第二版 Images – 將所有套件包在container內
- Stroage 、ACI、管控API及存取模型、如何管理存取狀況
- 畫面demo https://youtu.be/lhMu94uCzR0?t=1213
- Open Azure Notebook
- import os cluster_type = os.environ.get("STANDARD_DS3_V2", "GPU") compute_target = ws.get_default_compute_target(cluster_type)