> For the complete documentation index, see [llms.txt](https://playbooks.equalexperts.com/mlops-playbook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://playbooks.equalexperts.com/mlops-playbook/pitfalls-avoid/dont-forget-to-understand-the-at-inference-usage-profile.md).

# Don’t forget to understand the at-inference usage profile

If you are deploying your algorithm as a microservice endpoint it’s worth thinking about how often and when it will be called. For typical software applications you may well expect a steady request rate. Whereas, for many machine learning applications it can be called as part of a large batch process leading to bursty volumes where there are no requests for five days then a need to handle 5 million inferences at once. A nice thing about using a walking skeleton ([Create a Walking Skeleton/ Steel Thread](/mlops-playbook/practices/create-a-walking-skeleton-steel-thread.md)) is that you get an early understanding of the demand profile and can set up load balancing for appropriate provisioning.


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