MLOps Blog Series Part 3: Testing scalability of secure machine learning systems using MLOps


Written by Takuto Higuchi, Product Marketing Manager, Data and AI Marketing


The capacity of a system to adjust to changes by adding or removing resources to meet demand is known as scalability. Here are some tests to check the scalability of your model.


System testing

System tests are carried out to test the robustness of the design of a system for given inputs and expected outputs (for example, an MLOps pipeline, inference). Acceptance tests (to fulfill user requirements) can be performed as part of system tests.


A/B testing

A/B testing is performed by sending production traffic to alternate systems that will be evaluated. Statistical hypothesis testing is used to decide which system is better.


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