Many of you are familiar with the comprehensive range of AI solutions that Nvidia currently offers. With chips optimized for AI, PCB boards, AI software dev kits, and the Omniverse platform with Digital Twin capabilities, Nvidia provides almost everything needed to successfully build an AI infrastructure that reaches across edge devices, data centers, and clouds. From there, Nvidia enables the creation of sophisticated AI models using deep learning and implementation of inference and continuous federated learning in smart devices. Nvidia’s integrated approach to AI encourages widespread adoption of AI technologies across virtually any industry, empowering even small companies to create AI-powered products and services.
While Nvidia makes much of the AI challenge a proverbial snap, there are still barriers blocking many companies from going full AI. Most of those barriers can be summarized in one word: Data. As with most innovative computing solutions, data management remains as the less-than-glamorous business challenge keeping organizations from unlocking their full potential.
What Could Accelerate Real-World AI?
Amid all the excitement surrounding AI and its endless possibilities, it’s easy to forget that most companies could use help to move beyond warm-up tests and trials, even if they know how and where to use it. There are a few significant hurdles that need to be leapt before an organization can build and use their own “real-world” AI solutions:
- AI projects have immense data growth, potentially causing data costs to spiral out of control
- AI data is increasingly multi-sense rich data, including high resolution videos, and tends to be unstructured. That makes it nearly impossible to track
- Data that is scattered from edge devices to core data centers and clouds remains scattered, which makes it hard to access and correlate
Just a few years ago when “Big Data” was a big industry trend, Volume, Variety, Velocity, and Veracity (the 4 Vs of data) characterized some of the challenges. AI data is even more beholden to these challenges since the right inputs have a big influence on the quality of AI models. AI data needs to be:
- The Right Data in the Right Place
- Relevant to the Project
- In a Learnable Format
Between these common big data difficulties and the challenges more specific to AI, many companies interested in AI are realizing that their data isn’t actually AI-ready yet. What they need is the kind of end-to-end infrastructure that Nvidia provides and then match it with its “data twin”. This way, the right data will flow through a seamless blend of devices, data center, clouds, all with their own optimized storage, interconnects, and compute, and on to applications and transactions. Imagine that nirvana! To do that, data must be collected, organized, and ready for exploration, curation and training, across edge, core, and cloud.
That’s where Akridata’s platform comes in.
Akridata: The Perfect Companion for Nvidia Architecture
Akridata’s capabilities are a direct match for any AI solution architected using Nvidia’s technology. Nvidia’s expansive architecture spans edge, core, and cloud, moving, processing and generating data across each. As the World’s First Edge Data Platform, Akridata complements that end to end picture. It manages physically distributed data across all platforms and layers, meaning that any data created by the Nvidia AI ecosystem can rely on Akridata to keep it in optimal state of readiness.
In the Autonomous world, edge, core, and cloud need to form a single, seamless end-to-end infrastructure, cooperating on data processing, data communications, and data storage. According to Gartner, 75% of enterprise-generated data will soon be created and processed at the edge, meaning that the complete inclusion of Edge data in AI modeling will become the difference between a so-so and so-successful project.
Akridata pre-processes, catalogs, and prioritizes unstructured data at the edge to get relevant data more quickly, which helps train AI models more accurately. Akridata also keeps IT and storage budgets from skyrocketing by granting visibility into data sets, giving you the tools you need to eliminate superfluous or duplicate data.
If you’re interested in learning more about how Akridata complements Nvidia’s infrastructure, please feel free to reach out to us anytime!