1.
www.dotnetconf.net

2.
• 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@

3.
CloudRiches Solution Architect
Microsoft MVP
Study4TW
Azure Taiwan Speaker
Azure Bootcamp Speaker
.NET Conf Speaker
Web Development
Cloud Service

4.
• Triple A
• Triple Kill

5.
• 當一切需要“AI”
• Machine Learning .NET
• Future

6.
當一切都需要“AI”…

7.
• 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()
....

8.
• 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

9.
• Computer Vision 識別 乖乖 不成功
• 可以用Custom Vision

10.
真的這麽美好嗎？？？
我們接著往下看

11.
Cognitive Service的問題

12.
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")
}));

13.
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")
}));

14.
• AI != API
• AI != API
• AI != API
• AI != API
• AI != API
• AI != API
• AI != API
• AI != API
• AI != API

15.
Machine Learning .NET

16.
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… ;)

17.
Is this A or B? How much? How many?
How is this organized?
Would you like?

18.
• 讓開發Machine Learning Model變得簡單
• Developer也行

19.
• Python , R are great for ML and Data Science
• ML.NET complements the experience that AML Studio and
Cognitive Service Provide

20.
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

21.
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.

22.
TensorFlow
nuget
.NET Standard
.NET Core
.NET Framework

23.
• 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

24.
+ more!
Windows 10
Power Point
Excel
Bing Ads

25.
如何使用？

26.
Machine Learning
lifecycle
Historic
Data
Test /
Evaluate
Build & Train
Load Data
Transform
Data
Chose
Feature
and Label
data
Train using
Algorithm
Evaluate
Model

27.
Is this A or B? Which label should this issue be assigned?

28.
Load Data
Transform
Data
Chose
Feature
and Label
Train
using
Algorithm
Evaluate
Model

29.
• 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

30.
• 不過可以用第三方的Deep Learning Model
• 例如TensorFlow

31.
• 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

32.
• 目前的Pipeline不靈活
• 沒有强行別
• 名稱更符合慣用

33.
結語

34.
• 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

35.
• 新的API
• Model Builder (UI)
• 更多 Trainer
• 工具使用更加方便

36.
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

37.
• 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

38.
• 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

39.
Q & A
謝謝大家
幫我填個問卷：
http://to.alantsai.net/20180929-event-q