This document discusses using ML.NET for machine learning models in an Azure DevOps continuous integration and delivery pipeline. It introduces ML.NET and describes how to build, train, test, and deploy ML models as part of a normal application lifecycle. The document provides an example of including unit tests to validate data quality and deploying a trained ML model if the build pipeline passes tests. It also suggests future improvements like versioning datasets and databases as training data.

1. ML.NET Model
Lifecycle with Azure
DevOps
Marco Zamana
Senior Developer 4ward
Microsoft MVP AI

2. Thanks to

3. Who am I
marco_zamana
MarcoZama
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4. What is ML.NET?
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Machine learning for .NET applications
ML.NET runs on:
Windows, Linux, and macOS using .NET Core

5. Machine learning for .NET applications
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6. ML.NET CLI
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7. Normal application lifecycle – building
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8. Prepare Your Data Build & Train Run
Model consumption
End-user app
using the ML model
ML model ML model
Model creation
App/tools for training
the ML model
Datasets
Normal ML Workflow

9. ML model lifecycle
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10. This is our sample
ML model and
trainer console app
for the CI pipeline
The code
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11. If your build pipeline
passes all the tests you
defined, than you are
good to go and deploy
the just trained/built
ML.NET model.
Unit tests from Azure DevOps dashboard
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12. «Providing new data» is the trigger
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13. Unit tests help us
“bad data” == bad quality
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14. Azure files can help us for large dataset
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15. Azure DevOps self-hosted agent is better
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16. • Versioning datasets
• Databases as training data
• DevOps workflow Scenarios
• ML Model Versioning
• Integration with Azure ML and MLFlow
Improovement in the future
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17. Get started with ML.NET 1.0!
Get started with ML.NET here.
Tutorials and resources at the Microsoft Docs ML.NET Guide
Sample apps using ML.NET at the machinelearning-samples GitHub repo
Improovement
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19. Thank You!!!

20. Thanks to