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Copy file name to clipboardExpand all lines: articles/machine-learning/concept-endpoints-online.md
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@@ -51,7 +51,7 @@ Managed online endpoints are the recommended way to use online endpoints in Azur
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|Managed service |• Fully managed compute provisioning/scaling<br>• Network configuration for data exfiltration prevention<br>• Host OS upgrade, controlled rollout of in-place updates |• Scaling is limited<br>• User must manage network configuration or upgrade |
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|Endpoint/deployment concept |Distinction between endpoint and deployment enables complex scenarios such as safe rollout of models |No concept of endpoint |
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|Diagnostics and Monitoring |• Local endpoint debugging possible with Docker and Visual Studio Code<br>• Advanced metrics and logs analysis with chart/query to compare between deployments<br>• Cost breakdown to deployment level |No easy local debugging |
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|Scalability |Limitless, elastic, and automatic scaling |• Container Instances isn't scalable <br>• AKS v1 supports in-cluster scale only and requires scalability configuration |
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|Scalability |Elastic, and automatic scaling (not bound by the default cluster size)|• Container Instances isn't scalable <br>• AKS v1 supports in-cluster scale only and requires scalability configuration |
|Advanced ML features |• Model data collection<br>• Model monitoring<br>• Champion-challenger model, safe rollout, traffic mirroring<br>• Responsible AI extensibility |Not supported |
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@@ -88,7 +88,7 @@ The following table highlights the key differences between managed online endpoi
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|**Recommended users**| Users who want a managed model deployment and enhanced MLOps experience | Users who prefer Kubernetes and can self-manage infrastructure requirements |
|**Node maintenance**| Managed host OS image updates and security hardening | User responsibility |
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|**Cluster sizing (scaling)**|[Managed manual and autoscale](how-to-autoscale-endpoints.md) supporting added node provisioning |[Manual and autoscale](how-to-kubernetes-inference-routing-azureml-fe.md#autoscaling), supporting scaling the number of replicas within fixed cluster boundaries |
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|**Cluster sizing (scaling)**|[Managed manual and autoscale](how-to-autoscale-endpoints.md) supporting additional node provisioning |[Manual and autoscale](how-to-kubernetes-inference-routing-azureml-fe.md#autoscaling), supporting scaling the number of replicas within fixed cluster boundaries |
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|**Compute type**| Managed by the service | Customer-managed Kubernetes cluster |
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