ML.NET 1.0 release is the first major milestone of a great journey that started in May 2018 when we released ML.NET 0.1 as open source. ML.NET is an open-source and cross-platform machine learning framework for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Recommendation, Image Classification and more. This presentation provides an overview of the technology with demos run in a Deep Learning Virtual Machine running Windows Server 2016. Code examples are in C# and F# and run in Visual Studio Community 2019. This technology is ready for production implementation and runs on .NET Core. This presentation is the first of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI

1. Data and AI Scientist @ Microsoft
Cloud Solution Architect
US CTO Customer Success
@marktabnet

3. ML.NET Open Source Momentum
150K+
1,427
1,528
106

4. ML.NET Customers
Andy Gray, Executive Partner Evolution
Software Design, Inc.

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

8. “It has exquisite buttons …
with long sleeves …works for
casual as well as business
settings”{f(x) {f(x)
Machine Learning
“Programming the UnProgrammable”

9. f(x)
Model
Machine Learning creates a
using this data
Machine Learning
“Programming the UnProgrammable”

10. ML.NET 1.0
Machine Learning framework for building custom ML Models
Custom ML made easy
Automated ML and Tools (Model Builder and CLI)
Proven at scale
Azure, Office, Windows
Extensible
TensorFlow, ONNX and Infer.NET
Cross-platform and open-source
Runs everywhere

11. A few things you can do with ML.NET 1.0 …

13. 1. Data
Example
Comment Text Sentiment
Wow... Loved this place. 1
Crust is not good. 0
Not tasty and the texture was just nasty. 0
The selection on the menu was great. 1

14. 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
Wow... Loved this place.
Crust is not good.
Not tasty and the texture was just nasty.
The selection on the menu was great.
2. Transformers

15. Example
Estimator
Comment Sentiment
Wow... Loved this place. 1
Crust is not good. 0
Not tasty and the texture was just nasty. 0
The selection on the menu was great. 0
3. Estimators

16. Comment Text Sentiment
Wow... Loved this place. 1
Crust is not good. 0
Not tasty and the texture was just nasty. 0
The selection on the menu was great. 1
Yelp review dataset
Features (input) Label (output)
Sentiment Analysis
Is this a positive comment? Yes or no

19. https://fsharp.org/guides/machine-learning/

20. https://fsharp.org/guides/math-and-statistics/

21. https://dotnet.microsoft.com/apps/data/spark

22. https://dotnet.microsoft.com/learn/dotnet/architecture-guides

24. http://dot.net/ml
http://aka.ms/mlnetsamples
http://aka.ms/mlnetdocs
http://aka.ms/mlnet