Are you a developer? Are you a Architect? You must have the focus about on the application lifecycle! Building, maintaining, and continuously updating the end-user business application. No news here, but if we add also ML, for example a ML.NET model what we must to do? We need a update and extend a new type of lifecycle: Machine Learning Model Lifecycle.

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ML.NET Model Lifecycle with Azure DevOps
Machine Learning Model Lifecycle
Marco Zamana
Microsoft AI MVP
Senior developer 4ward
@marco_zamana
marco.zamana@4ward.it

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Organizer & sponsors
GetLatestVersion.it

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What is ML.NET?
Machine learning for .NET applications
ML.NET runs on:
Windows, Linux, and macOS using .NET Core

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Machine learning for .NET applications

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ML.NET CLI

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Normal application lifecycle – building

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ML model lifecycle

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The code
This is our sample ML
model and trainer
console app for the CI
pipeline

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Unit tests from Azure DevOps dashboard
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.

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«Providing new data» is the trigger

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“bad data” == bad quality
Unit tests help us

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Azure files can help us for large dataset

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Azure DevOps self-hosted agent is better

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Improovement in the future
• Versioning datasets
• Databases as training data
• DevOps workflow Scenarios
• ML Model Versioning
• Integration with Azure ML and MLFlow

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Improovement
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

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THANK YOU!