The document outlines a framework for deploying machine learning models using various Azure services such as Azure Kubernetes Service, Azure API Management, and Azure Databricks. It emphasizes the importance of tracking experiments, managing costs, and using metrics for model retraining. Deployment strategies involve utilizing GitHub Actions for CI/CD alongside Azure infrastructure for monitoring and security.

1. Machine-learning teams
External
teams
Development Deployment
External
application
Platform
Azure Kubernetes
Service
Azure API
Management
Azure
Key Vault
Azure Monitor
Azure Databricks
Azure Data Lake
Gen2
Machine Learning
flow tracking
server
Azure Cost
Management
Microsoft Entra ID
Azure Container
Registry
Call API
Source
data
Track
experiments
and register
models
Collect
metrics and
logs
Containerize model
Deploy
container
Azure Application
Gateway
Microsoft Defender
for Cloud
Azure DevOps and GitHub

2. Develop Build Deploy
Staging
Monitor
Production
Azure Databricks
GitHub
repositories
GitHub Actions
Azure Container
Registry
GitHub Actions
Azure Kubernetes
Service
GitHub Actions
Azure Kubernetes
Service
Azure Monitor
Azure Log
Analytics
workspace
Use metrics and logs to determine when model retraining needs to occur.
Manual
approval
check
Trigger
on push
or pull
request