2. • Interested in using machine learning (ML) in your
applications?
• Interested in using a ML framework built especially for
(.NET) developers?
2
3. • Brief introduction to machine learning
• Examples that ML might help to solve
• Options for using ML in your applications
• Introduction to ML.NET
• Demo
3
4. • Can be used to solve problems that may be otherwise
be very difficult to solve
• For example, write a function which takes an image and
tell me if it is a photo of a cat
• However, using ML, instead of writing code, we will
supply data (examples)
• ML looks for patterns and similarities in data to make
predictions on new datasets
4
6. How much / how many?
6
Examples:
• Predict the temperature
• Predict my taxi fare
• Predict house prices
• Predict next month’s sales
This Photo by Unknown Author is licensed under CC BY-SA
7. What type of thing is this?
7
• Binary – Is this A or B? Yes or No?
• Sentiment analysis
• Spam detection
• Multi-class – more than 2 types…
• GitHub Labeler
This Photo by Unknown Author is licensed under CC BY-SA-NC
8. Are there different groups?
Which does it belong to?
8
• Finding similar properties or features
in a data set
• e.g. Customer segmentation
This Photo by Unknown Author is licensed under CC BY-SA
9. • Anomaly detection
• Is this unusual?
• Recommendation
• Which option should I use?
• And more…
• Deep Learning / Neural Networks
• …
9
10. 10
Don’t re-invent the wheel…
Look for existing (prebuilt) trained models that may
already do what you need
e.g. Azure Cognitive Services
12. • Custom Vision Service
• Customize your compute vision models using your own labeled images
• Custom Speech Service
• Customize speech recognizer to suit user vocabulary
• Language Understanding (LUIS)
• Help predict overall meaning and intent of conversation
• Custom Decision Service
• Contextual decision making API
12
14. • Open sourced and cross-platform
• Machine Learning Framework (in preview)
• Built for .NET developers
• Local model creation and deployment (.zip file)
• Proven Framework behind Bing Ads, Windows Hello,
Excel Recommended Charts, PowerPoint Design Ideas,
and others
14
16. 1. Understand the problem (success criteria)
2. Prepare your data (training data, test data)
• Load the data
• Extract features (transform your data to numbers)
3. Build and Train
• Train the model using training data
• Evaluate the model with test data (successful?)
4. Deploy Model (use the model to make predictions)
16
21. Is this A or B? Which label should this issue be assigned?
https://myignite.techcommunity.microsoft.com/sessions/65908
22. • What label (tag) should this GitHub issue be assigned?
• What type of ML problem is this?
• Classification, multi-class
• What are my inputs (features)
• Title, Description
• What am I predicting? (output, label)
• Area / Tag
• What is my success criteria?
• Predict GitHub issue’s area tag with > 70 % accuracy
22
26. • Revolutionizing areas like computer vision and speech recognition
• Takes advantage of large amounts of data and compute
https://myignite.techcommunity.microsoft.com/sessions/65908
32. 32
• Brief introduction to machine learning
• Types of ML Problems
• Regression, Classification, Clustering
• Options for using ML in your applications
• Using or customizing pre-built models
• Training your own models in ML.NET
• Consuming pre-trained models in ML.NET from
TensorFlow and/or ONNX formats
