Deployment
• Web Service Deployment: Azure Machine Learning simplifies the
process of deploying models as web services, making them accessible
via HTTP requests. This deployment method is ideal for integrating
machine learning capabilities into applications or making them
available for further testing and validation.
• Deployment Options: Models can be deployed to Azure Kubernetes
Service for high-scale, production-grade applications or Azure
Container Instances for low-scale, cost-effective testing and
development scenarios. The process involves creating an inference
configuration, which specifies the environment and scripts needed
for the web service, and then deploying the model using the Azure
Machine Learning SDK or Studio.
Monitoring and Management
• Model Performance Monitoring: Once deployed, monitoring the
model's performance to detect any degrada on over me is crucial.
Azure Machine Learning offers tools for monitoring usage, response
mes, and accuracy. Se ng up alerts for significant performance
changes, ensures mely responses to poten al issues.
• Lifecycle Management: Managing the model lifecycle involves
versioning models to track changes, upda ng models with new data or
improved versions, and re ring models that are no longer needed.
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