The document discusses the integration of DevOps practices and GitHub capabilities for continuous and conditional deployment of AI products on Azure, with a focus on machine learning (ML) operationalization (MLOps). Key objectives include enhancing collaboration between data scientists and IT operations while automating the model development lifecycle. It outlines a solution architecture that features automated pipelines for model training, testing, and deployment, utilizing tools like GitHub Actions and Pulumi.

1. Approcci DevOps per il
Rilascio Continuo di Modelli
ML su Azure
Igor Antonacci – Automation Lover
Luca Milan – IT & DevOps addicted
Combinare le pratiche DevOps e le potenzialità di
GitHub per il rilascio continuo e condizionato
di prodotti di AI su Azure.

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3. Agenda
❑Introduzione
❑DevOps & Machine Learning = MLOps
❑Tools e Metodologie
❑Demo Time

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Computer Science
AI
Development
IT
Operations

5. ML: Peculiarità
Matrice
Matematica
Strumenti
Dedicati
Ciclo di
Vita
Modalità
Rilascio
Codice
Dati
Modello

6. ML: Ciclo di Vita
Training
Data
Learning
Process
Learned
Function
Scoring
or
Serving
Get fresh data and training again

7. Machine Learning senza Python

8. DevOps Pipeline 4 ML
Data or Algorithm
updated
Function Code
updated
Infrastructure
updated
ML.NET
Training
Deploy
Model
Pulumi Up
Deploy
Function
Yes
DevOps
Done
No
Build & Test
Triggers
C# Code

9. Scoring/Serving
Micro UI
ML Training
Predictor
Micro UI
Code + Data
Github Pipelines
(Re)Feed ML Pipeline
Solution
Overview

11. IGOR ANTONACCI

12. © Copyright Microsoft Corporation. All rights reserved.
Obiettivo organizzativo
All’interno di un Team di sviluppo, facilitare il dispiegamento di
Modelli di IA attraverso:
• la COOPERAZIONE tra Data Scientists e IT Operations
• L’AUTOMAZIONE del processo di Analisi, Sviluppo, Test e
rilascio di un modello

13. © Copyright Microsoft Corporation. All rights reserved.
MLOps
“With Machine Learning Model Operationalization Management (MLOps),
we want to provide an end-to-end machine learning development process
to design, build and manage reproducible, testable, and evolvable ML-
powered software.”
(cit. ML Ops: Machine Learning Operations (ml-ops.org))

14. Solution Architecture
Prediction Pipeline
v0.1.0-abcdef
Training Model Pipeline
Micro-UI Pipeline

15. Branching model
for Data Scientists
MAIN
experiment / model-refininig_001
experiment / ml-net_release-change_001
Pull Request
✓ Model
(Code)
✓ Test
✓ Dataset
✓ Review
✓ Approve
✓ Merge

16. GitHub Actions to the
rescue
COMMIT
✓ Model (Code)
✓ Test
✓ Dataset
✓ Build
✓ Test
✓ Training
Pull-request
✓ Review
✓ Approve
✓ Merge
Training Model

17. GitHub Environments
✓ on push
✓ on pull-request
✓ …
Job #1 Job #2
Action 1 – Setup .NET Core
Action 2 – Build project
Action 3 – Test project
Action N – …
Action 1 – GitVersion
Action 2 – Create Release
Action 3 – Publish Artifacts
Action N – …
Depends on
Secrets
✓ on push
✓ on pull-request
✓ …
Job #1 Job #2
Action 1 – Setup .NET Core
Action 2 – Build project
Action 3 – Test project
Action N – …
Action 1 – GitVersion
Action 2 – Create Release
Action 3 – Publish Artifacts
Action N – …
Depends on
Environment
Approvers

18. GitHub Environments
COMMIT
✓ Model (Code)
✓ Test
✓ Dataset
✓ Build
✓ Test
✓ Training
✓ Trained Model
✓ Dataset
Pull-request
✓ Review
✓ Approve
✓ Merge
Approvazione
Promozione
✓ Review
✓ Approve
Serving “Environment”
Training Model
Dispatch Message
▪ event_type: model-created
▪ message: |
{
"ML_MODEL_VERSION": vX.Y.Z-xxxx,
"ML_MODEL_URI":
https://github.com/igoran/.../${{
env.VERSION_NAME }}.zip
}

19. GitHub as Artifact
Repository
COMMIT
✓ Model (Code)
✓ Test
✓ Dataset
✓ Build
✓ Test
✓ Training
✓ Trained Model
✓ Dataset
Pull-request
✓ Review
✓ Approve
✓ Merge
Approvazione
Promozione
✓ Review
✓ Approve
Serving “Environment”
Dispatch Message
▪ event_type: model-created
▪ message: |
{
"ML_MODEL_VERSION": vX.Y.Z-xxxx,
"ML_MODEL_URI":
https://github.com/igoran/.../${{
env.VERSION_NAME }}.zip
}
Training Model
0.1.0-c28c8ba

20. GH Actions – Workflow
Dispatch
COMMIT
✓ Model (Code)
✓ Test
✓ Dataset
✓ Build
✓ Test
✓ Training
✓ Trained Model
✓ Dataset
Pull-request
✓ Review
✓ Approve
✓ Merge
Approvazione
Promozione
✓ Review
✓ Approve
Serving “Environment”
Training Model
Dispatch Message
▪ event_type: model-created
▪ message: |
{
"ML_MODEL_VERSION": vX.Y.Z-xxxx,
"ML_MODEL_URI":
https://github.com/igoran/.../${{
env.VERSION_NAME }}.zip
}

21. GH Actions – Workflow
Dispatch
SETUP INFRA
✓ Build
✓ Test
Azure
Deployment
Slots
model-created
✓ Trained Model
✓ Dataset
Download Model
PROD DEV
Predictor

22. MicroUI Pipeline
DEPLOY
✓ Build
✓ Test
FrontEnd Dev

23. Scoring/Serving
Micro UI
ML Training
Predictor
Micro UI
Code + Data
Github Pipelines
(Re)Feed ML Pipeline
Solution
Overview

24. © Copyright Microsoft Corporation. All rights reserved.
PULUMI
▪ Infrastructure-as-Code Framework
▪ Multi-Platform Providers (AWS, Azure, GCP … )
▪ (Multi-)Language oriented
▪ C#, Python, Go …
▪ CLI ed Engine multi piattaforma (pulumi)
▪ NO YAML ☺
▪ Ambienti “sandboxed” →Stacks

25. DEMO

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