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In all business categories, particularly in healthcare, the development of AI applications using public domain AI models and do-it-yourself architectures presents complex technical challenges. The rapid evolution of AI in the market makes it difficult for businesses to stay current and competitive. Additionally, compliance challenges and the need to protect confidential data have led enterprise customers to deploy various AI and GenAI workloads in on-premises, cloud-native, or hybrid cloud environments. Numerous decisions regarding platform, hardware, and AI model selection must be addressed to build customized, high-value applications. The following section outlines resources that require careful consideration.
Platform challenges
Choosing the right platform for AI workloads can be challenging due to the plethora of options, including the possibility of building your own. The ideal platform should be cloud-native and software-defined, offering seamless integration with hardware to manage, upgrade, and monitor. The platform should also provide scalability and ease of use for both developing and deploying AI workloads.