Massive AI Data at the Edge Brings Exascale-Class Data Problems to Boardrooms
Akridata, a category-maker and pioneer in Data-Centric Artificial Intelligence (AI), recently launched the Akridata Edge Data Platform, a software solution that creates and manages smart data pipelines and AI workflows spanning Edge-Core-Cloud resources – an industry first. The Akridata software solves the Exascale-class data problems that emerge when streams of rich data from physically scattered Edge devices create an avalanche of data that is impossible to organize, filter, access, and process.
There is a global race to produce the first Exascale supercomputer capable of performing over 1 “Exaflops,” 10 to the power 18 double-precision floating point operations per second (FLOPS). In 2019, the High Performance Computing (HPC) community registered Oct.18th (10/18) as the National Exascale Day in celebration of the new advances and discoveries that Exascale-class systems could provide.
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“At Akridata, we see a change in how and where Exascale problems materialize. They are increasingly led by data and are showing up in businesses across industries, not just in supercomputing,” said Kumar Ganapathy, co-founder and CEO of Akridata. “This change is driven by AI data at the Edge, which is taking Exascale-class data problems straight into boardrooms of businesses that are pursuing advanced automation and autonomy. Joining in the celebration of Exascale Day is a great way to recognize, share and track the latest developments and technologies, as well as organizations that are driving the Exascale Era.”
Akridata’s innovative new solution enables the integration of Deep Learning with Inference, Edge Commerce, Data Governance, and enterprise applications. It was specifically developed to accelerate real-world AI applications spanning industries including autonomous cars in the auto industry, AI-driven diagnoses in healthcare, as well as unattended manufacturing and more.
Exascale Data Problem of AI
The autonomous world depends on continuous Deep Learning using large volumes of complex AI data sets. Streams of rich data – such as video and lidar data – generated by fixed or mobile edge devices must be organized, filtered, tracked, and processed across the Edge-Core-Cloud resources. Massive amounts of data are being generated at the Edge by these devices.
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For example, a self-driving car in its test phase can generate multiple Terabytes of data in a single day. Now imagine tens of millions of cars with driver assist and self-diving technologies in real-world production generating Petabytes of data each day, all adding up to an Exascale data management challenge.
In addition, many other industries, such as unattended retail and smart cities, are also experiencing similar data explosion rates that need to be addressed. By 2025, 75 percent of the 175 Zettabytes of new data generated will come from the Edge, according to industry experts.
- Advanced AI and autonomy depend on the continuous curation and filtering of massive amounts of data generated at the Edge to fuel continuous Deep Learning. This is an Exascale-class data challenge.
- Akridata has created the industry’s only Edge Data Platform for Data-Centric AI, a new IT category that focuses on the structure and quality of AI data versus the repetitive refinement of complex AI models. Akridata provides a decentralized software layer that enables Data-Centric AI via smart data pipelines, automated workflows, on-demand access, and fast data exploration.
- The Akridata platform has been shown to deliver ten times faster time-to-access the right data, four times more efficient usage of compute and storage, and two times better productivity for data scientists and Machine Learning engineers.
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