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。
4.
5.
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?
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
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
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)