ML.NET Model Lifecycle with Azure DevOps - Devops heroes 2019
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The document discusses the lifecycle of ML.NET models in conjunction with Azure DevOps, highlighting the platforms it runs on and the typical application lifecycle. It emphasizes the importance of unit tests in ensuring quality and the use of Azure for handling large datasets. The document also suggests future improvements in areas such as dataset versioning and integration with Azure ML.
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ML.NET Model Lifecycle with Azure DevOps - Devops heroes 2019
- 1. #DOH19
- 2. 2 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
- 3. #DOH19 3 Organizer & sponsors GetLatestVersion.it
- 4. #DOH19 4 What is ML.NET? Machine learning for .NET applications ML.NET runs on: Windows, Linux, and macOS using .NET Core
- 5. #DOH19 5 Machine learning for .NET applications
- 7. #DOH19 7 Normal application lifecycle – building
- 8. #DOH19 8 ML model lifecycle
- 9. #DOH19 9 The code This is our sample ML model and trainer console app for the CI pipeline
- 10. #DOH19 10 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.
- 11. #DOH19 11 «Providing new data» is the trigger
- 12. #DOH19 12 “bad data” == bad quality Unit tests help us
- 13. #DOH19 13 Azure files can help us for large dataset
- 14. #DOH19 14 Azure DevOps self-hosted agent is better
- 15. #DOH19 15 Improovement in the future • Versioning datasets • Databases as training data • DevOps workflow Scenarios • ML Model Versioning • Integration with Azure ML and MLFlow
- 16. #DOH19 16 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
- 17. 17