Slides used during the session "Getting started with ML.Net" with the CatchIT community on April 10, 2020.

1. Getting started with ML.Net
Bruno Capuano
Innovation Lead @Avanade
@elbruno | http://elbruno.com

2. Deep Neural Network: Cat vs Dog
https://becominghuman.ai/building-an-image-classifier-using-deep-learning-in-python-totally-from-a-beginners-perspective-be8dbaf22dd8

3. Computer Vision

4. Why is this hard?
You see this:
But the camera sees this:

5. Computer Vision

6. DESKTOP CLOUDWEB MOBILE ML
.NET
IoTGAMING
Your platform for building anything

7. Windows 10 (Windows Defender)
Power Point (Design Ideas)
Excel (Chart Recommendations)
Bing Ads (Ad Predictions)
+ more
Azure Stream Analytics (Anomaly Detection)
Power BI (Key Influencers)
ML.NET is proven at scale, enterprise ready

8. Proven & Extensible
Open Source & Cross platform
dot.net/ml
Build your own
Developer Focused
ML.NET is a machine learning framework
made for .NET developers

9. And many more examples
@ https://github.com/dotnet/machinelearning-samples
Customer segmentation
Recommendations
Predictive maintenance
Forecasting
Issue Classification
Ranking news/topics
Image classification
Sentiment Analysis
Machine Learning scenarios with ML.NET

10. 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 machine learning models

11. Easy / Less Control Full Control / Harder
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
Vision Speech Language
Knowledge SearchLabs
Limitations with pre-built machine learning
models

12. Load Data
Extract
Features
Model
Consumption
Train
Model
Evaluate
Model
Prepare Your Data Build & Train Run
Machine Leaning workflow

13. Machine Learning.Net
Getting Started with Sentiment Analysis

14. Comment Toxic? (Sentiment)
==RUDE== Dude, you are rude … 1
== OK! == IM GOING TO VANDALIZE … 1
I also found use of the word "humanists” confusing … 0
Oooooh thank you Mr. DietLime … 0
Wikipedia detox data at https://figshare.com/articles/Wikipedia_Talk_Labels_Personal_Attacks/4054689
Features (input) Label (output)
Sentiment Analysis

15. Is this A or B? Is this a toxic comment?
Yes or no
Sentiment analysis explained

16. Prepare Your Data
Example
Comment Toxic? (Sentiment)
==RUDE== Dude, you are rude … 1
== OK! == IM GOING TO VANDALIZE … 1
I also found use of the word "humanists” confusing … 0
Oooooh thank you Mr. DietLime … 0
Important concepts: Data

17. Prepare Your Data
Text Featurizer
Featurized Text
[0.76, 0.65, 0.44, …]
[0.98, 0.43, 0.54, …]
[0.35, 0.73, 0.46, …]
[0.39, 0, 0.75, …]
Example
Text
==RUDE== Dude, you are rude …
== OK! == IM GOING TO VANDALIZE …
I also found use of the word "humanists” …
Oooooh thank you Mr. DietLime …
Important concepts: Transformer

18. Build & Train
Example
Estimator
Comment Toxic? (Sentiment)
==RUDE== Dude, you … 1
== OK! == IM GOING … 1
I also found use of the … 0
Oooooh thank you Mr. … 0
Important concepts: Estimator

19. Comment
==RUDE== Dude, you …
Prediction Function
Predicted Label – Toxic? (Sentiment)
1
Run
Example
Important concepts: Prediction Function

20. Demo: Sentiment Analysis
MakeMagicHappen();
https://www.avanade.com/AI

21. Machine Learning.Net
Anomaly Detection

22. Anomaly Detection
Anomaly detection detects data
points in data that does not fit well
with the rest of the data.
It has a wide range of applications
such as fraud detection, surveillance,
diagnosis, data cleanup, and
predictive maintenance.

23. Anomaly Detection
Hello World
MakeMagicHappen();
https://www.avanade.com/AI

25. Load Data
Extract
Features
Model
Consumption
Train
Model
Evaluate
Model
Prepare Your Data Build & Train Run
Machine Leaning workflow

26. Machine Learning.Net
AutoML and Model Builder

27. AutoML
Model
Builder
ML.NET Tooling
ML.NET CLI global tool accelerates productivity

28. How much is the taxi fare for 1 passenger going from Burlington to Toronto?
ML.NET CLI global tool accelerates productivity
AutoML with ML.NET

29. Criterion
Loss
Min Samples Split
Min Samples Leaf
XYZ
Parameter 1
Parameter 2
Parameter 3
Parameter 4
…
Distance
Trip time
Car type
Passengers
Time of day
…
Gradient Boosted
Nearest Neighbors
SGD
Bayesian Regression
LGBM
…
Distance Gradient Boosted
Model
Car type
Passengers
Getting started w/machine learning can be hard
ML.NET takes the guess work out of data prep,
feature selection & hyperparameter tuning
Which algorithm? Which parameters?Which features?
Getting started w/machine learning can be
hard

30. N Neighbors
Weights
Metric
P
ZYX
Criterion
Loss
Min Samples Split
Min Samples Leaf
XYZ
Which algorithm? Which parameters?Which features?
Distance
Trip time
Car type
Passengers
Time of day
…
Gradient Boosted
Nearest Neighbors
SGD
Bayesian Regression
LGBM
…
Nearest Neighbors
Model
Iterate
Gradient BoostedDistance
Car brand
Year of make
Car type
Passengers
Trip time
Getting started w/machine learning can be hard
ML.NET takes the guess work out of data prep,
feature selection & hyperparameter tuning
Getting started w/machine learning can be
hard

31. Which algorithm? Which parameters?Which features?
Iterate
Getting started w/machine learning can be hard
ML.NET takes the guess work out of data prep,
feature selection & hyperparameter tuning
Getting started w/machine learning can be
hard

32. 25%40%70%
25%
95%
25% 25%
25%
25%
40%
40%
40%
40%
70%
70%
70%Enter data
Define goals
Apply constraints
Input Intelligently test multiple models in parallel
Optimized model
95%
ML.NET accelerates model development

33. 70%95% Feature importance
Distance
Trip time
Car type
Passengers
Time of day
0 1
Model B (70%)
Distance
0 1
Trip time
Car type
Passengers
Time of day
Feature importance Model A (95%)
ML.NET accelerates model development
with model explainability
ML.NET accelerates model development

34. Demo: Auto ML
MakeMagicHappen();
https://www.avanade.com/AI

35. # STEP 1: Load data
IDataView trainingDataView = mlContext.Data.LoadFromTextFile<TaxiTrip>( ... )
IDataView testDataView = mlContext.Data.LoadFromTextFile<TaxiTrip>( ... )
ConsoleHelper.ShowDataViewInConsole(mlContext, trainingDataView)
# STEP 2: Initialize user-defined progress handler that AutoML will invoke after each model
var progressHandler = new RegressionExperimentProgressHandler()
# STEP 3: Run AutoML regression experiment
ExperimentResult<RegressionMetrics> experimentResult = mlContext.Auto()
.CreateRegressionExperiment(ExperimentTime)
.Execute(trainingDataView, LabelColumnName, progressHandler: progressHandler)
PrintTopModels(experimentResult)
# STEP 4: Evaluate the model on test data
RunDetail<RegressionMetrics> best = experimentResult.BestRun
ITransformer trainedModel = best.Model
IDataView predictions = trainedModel.Transform(testDataView)
# STEP 5: Save trained model to a .ZIP file
mlContext.Model.Save(trainedModel, trainingDataView.Schema, ModelPath)

36. Demo: Auto ML
MakeMagicHappen();
https://www.avanade.com/AI

37. Try ML.NET today!
http://dot.net/ml
http://aka.ms/mlnetsamples
http://aka.ms/mlnetdocs
http://aka.ms/mlnet
https://aka.ms/mlnetprod

38. Getting started with ML.Net
Bruno Capuano
Innovation Lead @Avanade
@elbruno | http://elbruno.com