Accelerated Artificial Intelligence: Is your Data Infrastructure Ready for AI at the Point of Action? has been saved
Perspectives
Accelerated Artificial Intelligence: Is your Data Infrastructure Ready for AI at the Point of Action?
Next gen architecture from a business service view
Once a vision is set for how the data architecture can be enhanced for high-performance computing and edge AI, the next step is to identify which pieces can be bought, which are better used as a service, and which could be built by the enterprise. These are not just technology considerations but instead they impact the wider business strategy and spending. Indeed, shifting the data architecture is a business decision.
Solving tomorrow’s challenges by accelerating AI innovation
Next gen architecture from a business service view
Ultimately, every robot is owned by a business function because they are automating, replicating or enhancing what would otherwise be a human worker. When a human is taken out of the loop in this way, the business function still requires data and insights computed at the point of action. This is what it means to shift from post-transactional reporting to data attenuating business process real-time AI. If we look at traditional data architectures, there are specific areas where a complementary suite of technologies can permit a model where operations are the primary data consumers.
With our ecosystem of technology partners, we can help you identify the right hardware and infrastructure that aligns with business strategy and goals, and we then work with you to implement the right tools to prepare your data infrastructure for a future with real-time AI and HPC.
Setting up your data infrastructure for real-time compute
Once a vision is set for how the data architecture can be enhanced for HPC and edge AI, the next step is to identify which pieces can be bought, which are better used as a service, and which could be built by the enterprise. These are not just technology considerations but instead they impact the wider business strategy and spending. Indeed, shifting the data architecture is a business decision. The complexity of the challenge and the enormous diversity of differentiating AI applications takes specialized domain expertise across technology ecosystems, systems integration, change management, and business strategy. Deloitte brings vertical specialization with cross-solution application in AI and HPC architecture to help drive the transformational shift away from post-transaction reporting to data attenuating business process at the point of action.
Succeeding with AI requires computational power. Using GPU-accelerated computing for model creation and deployment in application delivers essential time savings, higher accuracy, and a greater capacity for experimentation. As enterprises refine and expand their AI strategies, the clear call is to identify where accelerated computing can be used to enhance existing capabilities and accelerate the entire AI lifecycle.
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Christine Ahn
NVIDIA Chief Commercial Officer & Lead Client Service Partner
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