Amazon SageMaker multi-model endpoint (MME) enables you to cost-effectively deploy and host multiple models in a single endpoint and then horizontally scale the endpoint to achieve scale. As illustrated in the following figure, this is an effective technique to implement multi-tenancy of models within your machine learning (ML) infrastructure. We have seen software as a service (SaaS) businesses use this feature to apply hyper-personalization in their ML models while achieving lower costs.
For a high-level overview of how MME work, check out the AWS Summit video Scaling ML to the next level: Hosting thousands of models on SageMaker. To learn more about the hyper-personalized, multi-tenant use cases that MME enables, refer to How to scale machine learning inference for multi-tenant SaaS use cases.

In the rest of this post, we dive deeper into the technical architecture of SageMaker MME and share best practices for optimizing your multi-model endpoints.
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