2018 .NET Conf Taiwan Study4 Event http://study4.tw/Activity/Details/20 AI在生活中漸漸無所不在，市面上也越來越多服務串接及整合AI， 然而AI聰明取決於Prediction Model的建立，但是AI建立模型只能用R、Python嗎? ML.NET是為.NET開發人員構建的機器學習框架， 使用ML.NET能夠輕鬆的將自定義機器學習整合到您的應用程式之中。 這堂課將直接展示Machine Learning with .NET的威力，AI有夠潮，一起AI吧！ 講師介紹： Alan Tsai 沉浸於.NET 世界的後端工程師，除了最熟悉的Asp .Net MVC之外，喜歡學一些和程式開發有關的東西像Azure、Container、DevOps、Data Science等。 近年開始關注AI相關：Bot Framework (chatbot)、Cognitive Service、IoT Edge等。 樂於分享以及協助翻譯開源軟件，現任Study4成員之一。 除了寫程式以外，就愛看小說。 經歷 Study4 社群成員 2018 北京Azure Bootcamp講師 2017 .NET Conf 講師 相關連結 部落格：Alan Tsai的學習筆記 FB粉絲團：http://fb.alantsai.net Linkedin：http://linkedin.alantsai.net Twitter：http://twitter.alantsai.net Google+：http://plus.alantsai.net Alan Liu 曾經擔任ASP寫到ASP.NET的開發人員 正在修煉於Azure微軟雲的各式解決方案 參與社群到枕邊人都抓狂的社群狂熱者 經歷 CloudRiches Solution Architect Microsoft MVP Study4TW Azure Taiwan Speaker Azure Bootcamp Speaker .NET Conf Speaker 相關連結 部落格：維持熱情不滅

1. www.dotnetconf.net

4. • Alan Tsai
蔡孟玹
• 后端工程师
• 宏富云讯息科技有限公司
• Alan Tsai 的學習筆記
https://blog.alantsai.net
• contact@alantsai.net
alantsai.blog @alantsi2007 Line@

5. CloudRiches Solution Architect
Microsoft MVP
Study4TW
Azure Taiwan Speaker
Azure Bootcamp Speaker
.NET Conf Speaker
Web Development
Cloud Service

6. • Triple A
• Triple Kill

7. • 當一切需要“AI”
• Machine Learning .NET
• Future

8. 當一切都需要“AI”…

9. • 如果需要取得訂餐需求…….
• 傳統程式開發
• 全部列出來?
if(orderString.Contains("漢堡"))
{
...
}
else if()
....

10. • 有沒有這種東西….
f(x){ 我要一份漢堡餐可樂加大，
然後還要一份蘋果派，在
來2個布丁霜淇淋

12. • Computer Vision 識別 乖乖 不成功
• 可以用Custom Vision

15. 真的這麽美好嗎？？？
我們接著往下看

16. Cognitive Service的問題

17. Vision Speech Language
Knowledge SearchLabs
e.g. Sentiment Analysis using Azure Cognitive Services
96% positive
TextAnalyticsAPI client = new TextAnalyticsAPI();
client.AzureRegion = AzureRegions.Westus;
client.SubscriptionKey = "1bf33391DeadFish";
client.Sentiment(
new MultiLanguageBatchInput(
new List<MultiLanguageInput>()
{
new MultiLanguageInput("en","0",
"This is a great vacuum cleaner")
}));

18. Full Control / Harder
Vision Speech Language
Knowledge SearchLabs
e.g. Sentiment Analysis using Azure Cognitive Services
9% positive
TextAnalyticsAPI client = new TextAnalyticsAPI();
client.AzureRegion = AzureRegions.Westus;
client.SubscriptionKey = "1bf33391DeadFish";
client.Sentiment(
new MultiLanguageBatchInput(
new List<MultiLanguageInput>()
{
new MultiLanguageInput("en","0",
"This vacuum cleaner sucks so much dirt")
}));

19. • AI != API
• AI != API
• AI != API
• AI != API
• AI != API
• AI != API
• AI != API
• AI != API
• AI != API

21. Machine Learning .NET

22. Machine Learning
“Programming the UnProgrammable”
f(x)
Model
Machine Learning creates a
Using this data
But it needs a lot of sample training data in order to predict properly… ;)

23. Is this A or B? How much? How many?
How is this organized?
Would you like?

24. • 讓開發Machine Learning Model變得簡單
• Developer也行

25. • Python , R are great for ML and Data Science
• ML.NET complements the experience that AML Studio and
Cognitive Service Provide

26. Machine Learning
lifecycle
Historic
Data
Test /
Evaluate
Build & Train
ML model
file
(Trained)
Prepare Data, Build and Train an ML model Run/consume the ML model in app
Run /
Predict
?
Web apps /
Services
Mobile
apps
Desktop
apps
Bots
IoT

27. Machine Learning
lifecycle
Historic
Data
Test /
Evaluate
Build &Train
ML model
.ZIP file
(Trained)
Prepare Data, Build and Train an ML model
ML.NET API
(.NET Console app, etc.)
ML.NET Model Builder
(UI Desktop Tool)
ML Tasks, Data Transforms, Learners/Algorithms
ML.NET API
(.NET app)
API to run the model
nuget nuget
Business Application
(Web/Service/Desktop/Mobile)
ML model
file
.NET Core
or
.NET Framework
Predict: Run/consume the ML model
?
Live
User’s
Data
ML.NET
API to consume
the model
Run /
Predict
.NET Core
or
.NET Framework
.NET Core
or
.NET Framework
.csv files
etc.

28. TensorFlow
nuget
.NET Standard
.NET Core
.NET Framework

29. • 從 2018/05 發佈
• 每個月Release 1個版本
• 目前在 0.5
• 官方repo
https://github.com/dotnet/machinelearning

31. + more!
Windows 10
Power Point
Excel
Bing Ads

32. 如何使用？

33. Machine Learning
lifecycle
Historic
Data
Test /
Evaluate
Build & Train
Load Data
Transform
Data
Chose
Feature
and Label
data
Train using
Algorithm
Evaluate
Model

34. Is this A or B? Which label should this issue be assigned?

36. Load Data
Transform
Data
Chose
Feature
and Label
Train
using
Algorithm
Evaluate
Model

37. • 更多資料
• 不同的資料處理方式
• 選擇不同的Feature
調整Data
• 不同的演算法
• 不同的參數
調整
Algorithm
Load Data
Transform
Data
Chose
Feature
and Label
Train
using
Algorithm
Evaluate
Model
80%所花的時間

40. • 不過可以用第三方的Deep Learning Model
• 例如TensorFlow

41. • 使用TensorFlow的Model作爲開始
• 使用Inception V3 Model
• Sample
https://github.com/dotnet/machinelearning-
samples/tree/master/samples/csharp/examples/DeepLearning_TensorFlowMLN
ETInceptionv3ModelScoring
• ML.NET 0.5 有問題 -> 要用 0.6 Preview

42. • 目前的Pipeline不靈活
• 沒有强行別
• 名稱更符合慣用

43. 結語

44. • 專門為開發者設計
• 用來建立Custom Model的Framework
UI tool,
easy to get started
for .NET developers
(*) To be released
.NET code-first
approach to build
& train custom
models

45. • 新的API
• Model Builder (UI)
• 更多 Trainer
• 工具使用更加方便

46. AI, ML and DeepLearning
technologies
Client apps
Bots
(Bot Framework)
Web apps
(ASP.NET)
Mobile apps and IoT Edge devices
(Xamarin) (IoT Edge SDKS)
Consume
(Pre-built AI: Ready to use)
Azure
Cognitive Services
Pre-trained models
(ONNX, CoreML, WindowsML)
Visual Studio and .NET
Easier / Less control Harder / Full control
Build your own
(Custom AI)
ML.NET TensorFlow,
CNTK,
Torch,
ONNX, etc..
Azure Machine
Learning
Studio
Integration

47. • ML.NET 相關
https://github.com/dotnet/machinelearning
https://github.com/dotnet/machinelearning-samples
https://docs.microsoft.com/en-us/dotnet/machine-learning/
https://blogs.msdn.microsoft.com/dotnet/tag/ml-net/
https://channel9.msdn.com/Events/dotnetConf/2018
https://channel9.msdn.com/Events/Build/2018

48. • Demo的Sample程式碼
https://to.alantsai.net/code-mlnet
• Machine Learning / Cognitive Service / Chatbot
https://to.alantsai.net/blog-datascience
https://to.alantsai.net/blog-chatbot-with-ai
https://to.alantsai.net/blog-face-api

50. Q & A
謝謝大家
幫我填個問卷：
http://to.alantsai.net/20180929-event-q