From the course: Cloud-Based AI Solution Design Patterns

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Data-centric design patterns overview

Data-centric design patterns overview

From the course: Cloud-Based AI Solution Design Patterns

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Data-centric design patterns overview

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- An AI system's data architecture that resides within or extends to a cloud environment is primarily distinguished by the underlying cloud infrastructure that it has access to. Whereas on-premises data architectures for AI systems generally need to work within an infrastructure that has fixed boundaries and limitations, a data architecture in a public cloud will have the flexibility to dynamically scale both in terms of data storage and in terms of the compute power required for data processing. Cloud environments can also offer greater resiliency by better protecting databases and data processing programs from runtime failure conditions. This includes more rapid recovery from runtime failure, so as to minimize or avoid service disruptions. Another important factor is the sophisticated level of automation that many cloud providers can offer. Often, on-premises data architectures have a greater need for IT administrators to manually maintain the systems, platforms, and data sources…

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