juniorAzure ML
What is Model Deployment in Azure ML?
Updated May 15, 2026
Short answer
Model deployment exposes trained models as scalable prediction services.
Deep explanation
Azure ML supports deploying machine learning models as online or batch endpoints. Managed online endpoints provide real-time inference, while batch endpoints process large asynchronous workloads.
Deployment packages typically include:
- Trained model artifacts
- Scoring script
- Environment dependencies
- Inference configuration
Azure ML supports deployments to managed infrastructure, AKS, and edge devices.
Real-world example
A banking application deploys fraud detection models as REST APIs for real-time transaction scoring.
Common mistakes
- Ignoring monitoring, failing to configure autoscaling, and deploying without testing latency.
Follow-up questions
- What is a scoring script?
- What are batch endpoints?
- Why monitor deployed models?