Enterprise adoption of artificial intelligence (AI) is growing rapidly, having reached 35% in 2022 and showing no signs of slowing down. This trend is unsurprising given the near-limitless potential for AI to streamline operations, improve decision-making and drive innovation. However, as adoption grows, there is also an increasing number of AI projects that fail to deliver value. AI initiatives can fall short of expectations for a number of reasons, such as process inefficiencies or skills shortages. How can we help?
AI at scale with DGX and Kubeflow
Running AI at scale requires an effective ML stack. It includes infrastructure and application layers that are individually sophisticated, and also integrate and work together to unlock their respective potentials. Neither layer can deliver its full value without the other. This is where Charmed Kubeflow and NVIDIA DGX systems come in.
AI at scale use cases
From telco to retail, from aerospace to public sector, there are various use cases that are ready to run AI at scale. Computational fluid dynamics (CFD) is the process of predicting the flow of liquids and gases through complex mathematical modelling and computation. The method uses numerical algorithms to solve fluid flow equations that involve thousands of small 3D cells, known as a computational mesh. From challenges to how AI can help, this whitepaper goes in depth of a specific use case that affects various industries.
Why read this whitepaper?
By going through this whitepaper, you will learn how to move further than experimentation. As a joint effort between NVIDIA and Canonical, this will help you will:
- Build a performant end-to-end ML stack
- Get you familiar with open source ML tooling
- Integrate easier the infrastructure and application layer to get better results
- See how it applies to computational fluid dynamics (CFD)