Definition
Deploying to Azure involves utilizing cloud-native tools like Azure Machine Learning, Azure Kubernetes Service (AKS), or Azure Functions to serve ML models at scale. It encompasses managing compute resources, ensuring high availability, implementing CI/CD pipelines for model updates, and monitoring performance metrics. This practice enables organizations to leverage Azure’s global infrastructure for robust and secure AI application delivery.
Summary
The process of hosting and running machine learning models on Microsoft Azure cloud infrastructure services.
Key Concepts
- Cloud Infrastructure
- Model Serving
- Scalability
- CI/CD Pipelines
Use Cases
- Real-time API endpoints for inference
- Batch scoring of large datasets
- Enterprise-grade model management