How AI Infrastructure Can Streamline Your AI Production Line
Deep learning and AI applications that rely on large amounts of unstructured data, like autonomous vehicle development and natural language processing, face unique challenges. NVIDIA DGX SuperPOD with DDN A3I storage can boost productivity by 20% while lowering costs compared to existing IT infrastructure or cloud-based deployments.
The interest in deep learning and AI as a way to tap into massive amounts of unstructured data continues unabated. While some companies were built with deep learning and AI at the center of their value, like autonomous vehicle companies and some financial organizations, many institutions are still working out how to capture and extract all the value that exists in their collection of unstructured data – and strategizing on how to collect and analyze new source data. This means that for each company poised to revolutionize markets because they were founded around machine learning and AI, there are hundreds of companies that are still assessing the potential value in their data and haven’t begun to understand the AI infrastructure choices that need to be made.
Streamlining Applications with AI Infrastructure
To de-risk a company’s journey to successful AI transformation, a strategic approach must be adopted toward AI infrastructure, and this approach is often informed by the nature, size, and timeliness of the application and the data that informs it.
The fundamental questions regarding data and how it will help drive your AI infrastructure choices are:
- Is the data the company needs already available in your organization, and what are the correct sources?
- Is the data accessible and centrally managed?
- Is an appropriate level of data governance in place?
- Are the datasets relatively small or large?
- Is the data available to the right people with the right tools?
- Do you have the right platform to process the data efficiently?
The answers to these questions will help drive the strategic decisions around whether to make or buy your AI application. They’ll inform whether cloud or on-premises deployment is appropriate. And they will help you understand the existing bottlenecks around data.
Many of DDN’s customers have concluded that, for AI applications at the core of their business, deploying infrastructure on premises is the most efficient way to manage their data and maximize application throughput. At DDN, we accelerate customers’ digital transformation journey by delivering the fastest path to implementation of AI infrastructure, helping them move seamlessly from prototype to production.
By choosing to deploy DDN’s A3I with NVIDIA DGX SuperPOD, you can leapfrog many of the other learnings that our customers have had with their AI applications. One of the main learnings is that customers’ existing infrastructure often can’t handle the data requirements needed to deed large-scale models. Institutions starting or expanding AI projects today have access to an increasingly large number of optimized tools and platforms that make it easier and faster to deploy AI at scale. With the experience of deploying storage systems behind thousands of NVIDIA DGX systems wall around the world, customers that choose DDN are working with a vendor that has the experience and expertise gleaned through years of working with large and diverse datasets across many industries.
Implementing tried and tested AI infrastructure means you can focus on the other key issues at hand for AI success – ensuring that the data collected is adequate and accurate, addressing any AI talent gaps on your staff, and ensuring your AI process is in place to extract the maximum value from your data.
To learn more about DDN’s A3I with NVIDIA DGX SuperPOD, and how consultants like Deloitte are assisting customers with streamlined AI Infrastructure to accelerate application development, check out Dr. James Coomer’s session at NVIDIA GTC: Selene and Beyond: Solutions for Successful DGX SuperPODs.