Scale Endpoint Guide

Why Scale Your Endpoint?

As your production workload grows, scaling your Azure Machine Learning endpoint ensures optimal performance, cost-efficiency, and reliability.

Key Concepts

• Auto-scaling: Automatically adjust resources based on demand.
• Horizontal Scaling: Add more instances of the endpoint.
• Vertical Scaling: Increase the resources of existing instances.

Steps to Scale

1. Identify the workload: Determine your peak demand and typical usage patterns.
2. Choose a scaling strategy: Select the most appropriate strategy (auto-scaling, horizontal scaling, vertical scaling).
3. Configure monitoring: Set up monitoring to track resource utilization and performance.
4. Implement autoscaling: Configure the endpoint to scale based on predefined thresholds.
5. Test and optimize: Regularly test and optimize the scaling configuration.

Resources

You can find more detailed information on Azure's official documentation at:

• [https://learn.microsoft.com/en-us/azure/machine-learning/scaling-endpoints?view=azure-docs](https://learn.microsoft.com/en-us/azure/machine-learning/scaling-endpoints?view=azure-docs)