Whitepapers

For supreme AI data management, the AI data lifecycle needs a clear, continuous flow, both for training of AI models, and for applying AI models to real time decision making.

Organizations need to adopt a disciplined approach to implementing and measuring the outcomes of AI projects.

AI engineering is an emerging discipline that focuses on applying tools, systems, and processes to enable AI to perform effectively.

The NVIDIA DGX SuperPOD™ is a first-of-its-kind artificial intelligence (AI) supercomputing infrastructure.

The DDN A³I scalable architecture integrates DGX A100 systems with DDN AI shared parallel file storage appliances and delivers fully optimized end-to-end AI, Analytics and HPC workflow.

Data now plays such a prominent role in determining business success that 98% of organizations surveyed by ESG are in some phase of data-driven digital transformation.

Although the promise and attraction of AI is well appreciated by IT and business decision-makers alike, it’s far less clear how organizations should begin their journey to enterprise-class AI.