This document provides an overview of the speaker's background and experience as well as an introduction to machine learning and how it can be used with .NET applications. The speaker has a master's degree in information education and has been a Microsoft MVP for 7 consecutive years. They discuss consuming pre-built AI models with Azure Cognitive Services and APIs or building custom machine learning models with ML.NET. Examples are provided of sentiment analysis, face detection, and building pipelines in ML.NET. Resources for learning more about artificial intelligence and machine learning in .NET are listed at the end.

2. About Me
Web: codeian.idv.tw
Email:
ian.msmvp@outlook.com
Blog: dotblogs.com.tw/ian
• 高師大資訊教育研究所碩士
• 連續7屆 Microsoft MVP
• Microsoft 大型研討會講師
• 巨匠電腦講師
• K.NET 社群講師兼共同創辦人
• 企業技術內訓講師
• 學校業界講師

5. Machine Learning

6. f(x)
Model
Machine Learning creates a
Using this data
Machine Learning
需要大量的樣本做為前置訓練 – 監督式學習

8. Machine Learning
Linear Discriminant Analysis
Regression
Neural Networks
(Autoencoders,
Self-organizing maps, etc.)
監督式學習(Infers label) 非監督式學習(Infers structure)
Topic Modeling
Dimensionality Reduction
Neural Networks
(MultiLayer Perception, etc.)
Topic modeling
Structured prediction
Decision Trees
Naïve Bayes
Binary Classification
Multi-class Classification
Clustering
Anomaly detection
Latent variable models
Linear regression
Logistic regression
k-nearest neighbor
Support Vector Machines
( K-means
Mixture models
Hierarchical clustering)

9. What AI, Machine Learning and Deep
Learning technologies can you use in .NET
applications?

10. 針對.NET 應用的AI & ML 彈藥
AI, ML and DeepLearning
technologies
Consume pre-built/pre-trained models or build your own custom model?
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

11. Pre-built AI:
Using Azure Cognitive Services
in .NET applications

12. Vision
From faces to
feelings, allow
your apps to
understand
images and video
Speech
Hear and speak
to your users
by filtering noise,
identifying
speakers, and
understanding intent
Knowledge
Tap into rich
knowledge amassed
from the web,
academia, or your
own data
Language
Process text and
learn how to
recognize what
users want
Labs
An early look at
emerging Cognitive
Services
technologies:
discover, try and
give feedback on
new technologies
before general
availability
Search
Access billions of
web pages, images,
videos, and news
with the power of
Bing APIs

13. Microsoft Cognitive Services
$.ajax({
url:
"https://southeastasia.api.cognitive.microsoft.com/face/v1.0/detect?"
+ $.param(params),
beforeSend: function (xhrObj) {
// Request headers
xhrObj.setRequestHeader("Content-Type", "application/octet-
stream");
xhrObj.setRequestHeader("Ocp-Apim-Subscription-Key",
apitoken);
},
type: "POST",
data: makeblob(face),
processData: false
})
Cognitive Service use API

14. Microsoft Cognitive Services
using Microsoft.ProjectOxford.Face;
using Microsoft.ProjectOxford.Face.Contract;
string subkey = "YOUR_SUBSCRIPTION_KEY";
var client = new FaceServiceClient(subkey);
Cognitive Service use SDK

15. Demo Cognitive Services
Face API
Chatbot LUIS
Custom Vision

16. Easy / Less Control Full Control / Harder
Vision Speech Language
Knowledge SearchLabs
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")
}));
e.g. Sentiment Analysis using Azure Cognitive Services
96% positive
Pre-built ML Models (Azure Cognitive Services)

17. Easy / Less Control Full Control / Harder
Vision Speech Language
Knowledge SearchLabs
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")
}));
e.g. Sentiment Analysis using Azure Cognitive Services
9% positive
Pre-built ML Models (Azure Cognitive Services)

18. 如果可以客製會不會更美好……….

19. 建置ML Model的作業流程
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

20. Azure Machine Learning
Studio

21. Azure Machine Learning Studio

22. Azure Machine Learning Studio

23. Demo Azure ML Studio

24. Custom Machine Learning
Using ML.NET

25. Machine Learning
framework made for .NET
developers

26. ML.NET目前可以處理哪些問題?
Is this A or B? How much? How many? How is this organized?

27. Mapping from Problems to ML Tasks
Something is A or B
Detect issues/problems
Group similar objects into sets
Predict relevance of objects
Predict values based on time/seasons
historic data
Predict value (price, forecast, etc.)
Advice on products/movies/etc.
Classify things across multiple categories
Clustering
Recommendations
Multi-class Classification
Regression
Binary Classification
Time Series
Ranking
Anomaly Detection
Problems ML Tasks

28. ML.NET is a framework for custom ML
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.

29. ML.NET is a framework
TensorFlow
nuget
.NET Standard
.NET Core
.NET Framework

30. ML.NET
.NET code-first approach to build & train custom models
f(x)
Model

31. ML.NET
UI tool,easy to get started for .NET developers
(*) To be released
f(x)
Model

32. ML.NET Pipelines

33. ML.NET Pipelines

34. ML.NET Pipelines

35. ML.NET Pipelines

36. ML.NET Pipelines

37. ML.NET Pipelines

38. Demo ML.NET

40. • ML.NET
https://github.com/dotnet/machinelearning
• Artificial Intelligence and Machine Learning for
Every .NET Developer
https://channel9.msdn.com/Events/dotnetConf/2018/S202
• Machine Learning in .NET
https://channel9.msdn.com/Events/dotnetConf/2018/S201
• ML.NET Doc
https://www.microsoft.com/net/apps/machinelearning-ai/ml-
dotnet
Resources