From the course: Cloud-Based AI Solution Design Patterns
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Containerized AI-model deployment
From the course: Cloud-Based AI Solution Design Patterns
Containerized AI-model deployment
- AI models are typically viewed as parts of an overall AI system containing the logic that gets executed by the system's inference engine. But as with any distributed application, it sometimes makes sense to look at how different components of the application may be able to benefit from being separated so that they can be scaled and managed independently. This is especially the case with a critical component such as the AI model. The containerized AI model deployment pattern essentially provides us with the option of putting a model together with its dependencies into a container. Using an appropriate underlying platform such as an orchestration platform, we can then create instances of the same containerized model. Why would we want to do this? Well, in addition to being able to scale the model dynamically in cloud environments, it also helps guarantee its availability and further supports efficient resource allocation as instances of the containerized model can be created and…
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