This document provides an overview of machine learning and ML.NET, a cross-platform machine learning framework for .NET developers. It defines machine learning as a field that allows computers to learn without being explicitly programmed. It then discusses ML.NET, describing it as an open source framework that makes custom machine learning easy using tools like the ML.NET Model Builder. It also provides examples of how ML.NET is used internally by Microsoft products and externally by customers.

1. Machine Learning
para devs com
ML.NET
@LetticiaNicoli

2. Olá!
Eu sou a Letticia Nicoli
o Software Engineer no @Nubank
o Microsoft MVP – Xamarin, .NET
o Organizadora do @High5Devs
o Cursando MBA em AI & ML
@LetticiaNicoli

3. Machine Learning
O que é essa “buzzword”?

4. “
Campo de estudo que dá aos computadores a
habilidade de aprender sem serem
explicitamente programados.

6. Datasets
ML model ML model
App/tools for training
the ML model
End-user app
using the ML
model
Prepare your data Build & Train Run
Model Creation Model Consumption
Machine Learning Workflow

7. ML.NET
■ Um framework para machine learning open source e
cross-platform.
http://dot.net/ml
Built for
.NET developers
Custom ML made
easy with tools
Extended with
TensorFlow & more
Trusted &
proven at scale
C#
F#

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

9. § Frameworks suportados:
○ .NET Core (Natively)
○ .NET Framework (Natively)
○ Python with NimbusML (Python bindings)
ML.NET runs anywhere

10. A few things you can do with ML.NET …
And more! Samples @ https://github.com/dotnet/machinelearning-samples

11. Formas de utilizar o ML.NET
C#
ML.NET
API
(Code)
ML.NET
Model Builder
(Visual Studio UI)
>_
ML.NET
CLI
(Command-Line
Interface)

12. ML.NET Model Builder
• Interface simples para criação
de modelos com AutoML;
• Carregamento de arquivos e
banco de dados;
• Geração de código para
treinamento e consumo.
http://aka.ms/mlnetmodelbuilder
AutoML: Classificação binária, multi-classe e regressão

13. ML.NET CLI
• Criação de modelos com AutoML;
• Cross platform
(Windows, Linux, MacOS);
• Geração de código para
treinamento e consumo.
> mlnet auto-train --ml-task binary-classification --dataset
"customer-reviews.tsv" --label-column-name Sentiment
macOS / Linux (Bash)
Windows (PowerShell e CMD)

14. Demo
Análise de Sentimentos
ML.NET CLI

15. Alta performance e acurácia

16. Microsoft Defender – Antivirus Threat Protection
Power Point - Design Ideas
Excel - Chart Recommendations
Bing - Ad Predictions
Azure ML Studio – Multiple components
Azure Stream Analytics - Anomaly Detection
Brenmor – Medical patient survey classification
Sig Parser – E-mail spam detection
Williams Mullen – Law document classification
Evolution Software – Hazelnut drying time
prediction
endjin – Newsletter article classification
Microsoft Products
(Using ML.NET internally for > 8 years)
Customers
(ML.NET v1 since May 2019)

17. Outras features importantes no ML.NET
• DevOps & ML Model lifecycle with Azure DevOps CI/CD pipelines
• Model Explainability and Feature input variables importance
• Cross-validation of ML.NET models:
• Loading data from ‘anywhere’ through the LoadFromEnumerable() method
• Using huge and sparse datasets (thousands or millions of columns)
• Deploy ML.NET model into high scalable and multi-threaded ASP.NET Core apps and services
• Deploy ML.NET model into an Azure Function
• Plus many more ML Tasks and scenarios (see samples here: https://github.com/dotnet/machinelearning-samples )
such as: Sentiment analysis, Spam detection, Fraud Detection, text classification, products recommendations, data
spike detection, clustering, ranking results, etc.
https://aka.ms/mlnetyoutube
Check out many of those scenarios at the ML.NET YouTube playlist:

18. ML.NET Resources
http://dot.net/ml
http://aka.ms/mlnetsamples
http://aka.ms/mlnetdocs
http://aka.ms/mlnet
https://aka.ms/mlnetyoutube

19. Obrigada!
Dúvidas?
@LetticiaNicoli
letticia.nicoli@gmail.com