Seeing technology challenges ahead of most – and solving them – is what the founders of Akridata do best. The founding team of this three-year-old company, led by serial entrepreneurs Kumar Ganapathy, CEO, and Vijay Karamcheti, CTO, include data scientists, software product developers and engineers, collectively holding nearly 200 patents. They are pioneers in advanced data management, who have created and brought to market solutions tackling some of the most perplexing data challenges on the planet.
These leaders were behind the successful start-up Virident, which was acquired in 2013 by Western Digital, part of Hitachi Global Storage Technologies. At Virident, they solved the then perplexing data storage problem to help enterprises handle performance-intensive data center applications with PCIe-based enterprise flash storage solutions for virtualization, database, cloud computing and webscale applications.
Now this same team, complemented by a strong group of AI data specialists, is tackling the biggest data challenge that is so crucial to advancing Artificial Intelligence (AI) that will drive the next phase of human progress. AI is tackling self-driving cars, unattended retail, and smart cities, but will go well beyond that to transform the world. We simply call it The Autonomous World. It’s coming, and it has a massive Exascale-class data problem that Akridata helps you solve.
What is the Autonomous World?
Autonomy is the next phase of AI and one of the most significant endeavors in human history and the data problem associated with it represents a giant challenge – an avalanche of data generated at the Edge. At the center of the Autonomous World are self-driving cars, unattended retail stores, smart manufacturing plants and cities, and AI-enabled healthcare, among other significant innovations. These Edge devices will produce and consume most of the World’s data by 2025 – that’s only four years away! And the massive data generated at the Edge is becoming simply too large to be sent to the cloud or to be held locally.
The volume, variety, and velocity of this rich data – typically video and lidar data — is creating an ExaScale data challenge demanding a new decentralized data management approach with smart technology to support automated workflows.
Data-Centric AI
This new world relies on Edge data, AI models built with Deep Learning, and using new data from to continuously improve AI models. Because edge devices are scattered around the world and can be fixed or mobile, an avalanche of Edge data flows through resources from Edge to Core to Cloud – sometimes multi-Terabytes of rich data per day for just one self-driving test car. This Edge data must be ingested, filtered, tracked, explored, and be retrievable quickly and cost-effectively.
The Akridata team could see that algorithm development, deep learning, and cloud technologies were making rapid progress but decentralized edge, where almost all the data is generated and ultimately used, was a glaring gap. They knew that data will become the most critical part of AI and it needs a solution that is designed as a distributed platform. Not cloud-centric or edge-centric, but data-centric. And that’s exactly what has happened.
The realization that data has become the most critical part of AI now has led to a new phase in AI’s progress: Data-Centric AI.
Akridata sees Data-Centric AI getting built on a foundation that is designed for the end-to-end device-to-cloud or service-to-cloud spectrum. a decentralized platform that understands
This story posted by Datanami in Sept. 2021 cites core elements of this pervasive edge data problem.
Data today doesn’t exist behind firewalls or within on-premises locations. It is globally distributed in siloed databases, data warehouses, and more across on-premises, network edge, and multiple clouds. To unlock value, applications need access to that data to contextualize it and correlate it across different datasets.
According to this eWeek story also just published in Sept. 2021, the trifecta is Edge Computing, Edge Networking and Edge Data, citing that that third element really hasn’t been solved.
Until today. Meet Akridata, aptly named as Akri means Edge in Greek, and its Decentralized Data Platform that serves as the foundation for Data-Centric AI in massive new ways.
Foresight: Three Key Happenings in 2015
Our founding leaders saw this big data challenge coming in 2015, when they started to see the Autonomous World taking shape. Three significant happenings were falling into place:
- The advent of the automation era, where the physical world meets the digital world at the Edge
- The proliferation of cameras generating massive video and lidar data
- The emergence of Deep Learning, with its continuous integrated inference and learning cycle
They knew that a new data management system was going to be needed to support the rapidly evolving Autonomous World and its new and unique needs. They saw the challenge first in the auto industry with ADAS/AV initiatives, but also knew other industries with full-force streams of video data and autonomous devices at the Edge would all also have similar data management requirements.
They realized powering the Autonomous World requires locating the data precisely relevant to specific situations; finding the correct scene among tens of thousands of video clips; comparing different AI algorithms according to the data sets that trained them; and cataloging the data so that one can find and retrieve what’s needed wherever the data, or the autonomous device, may reside.
A new data management solution needed to be invented to handle the flood of data at the Edge and to smartly organize, store and allow access to the data in a decentralized manner across the Edge, the Core and the Cloud.
These new data management requirements are solved with the Akridata Edge Data Platform (link to the product blog post for more details) to expertly ingest, catalog, explore, and track AI data in a decentralized approach. The firm’s customers have varying use cases and Akridata has been proven to deliver 10-fold faster time-to-access the right data; four-fold increase in compute/storage efficiency; and two-fold productivity increase for data scientists.
A Culture of Solving Challenges
Our co-founders, Kumar and Vijay, lead Akridata with an open culture so team members feel free to challenge ideas — crucial to solving the world’s toughest problems. They understand technologies and markets and the stable force each has on the other. They saw a massive need – the white space — in the market and set out to fill it. They take stock in the Stockdale Paradox, a POV that centers on balancing the right amount of optimism with realism – hope for the best, while prepping for the worst.
With great vision, this team worked across the past few years to build and prove out the Akridata solution with a rigorous day-to-day approach, applying Agile software development method and a powerful conviction for its thesis – that Data Centric AI can be enabled with a decentralized data management solution.
Click here to schedule a consult and learn how Akridata can fuel your AI innovation today.
Akridata Founders’ Story: Seeing AI’s Edge Data Challenge and Solving It
Seeing technology challenges ahead of most – and solving them – is what the founders of Akridata do best. The founding team of this three-year-old company, led by serial entrepreneurs Kumar Ganapathy, CEO, and Vijay Karamcheti, CTO, include data scientists, software product developers and engineers, collectively holding nearly 200 patents. They are pioneers in advanced data management, who have created and brought to market solutions tackling some of the most perplexing data challenges on the planet.
These leaders were behind the successful start-up Virident, which was acquired in 2013 by Western Digital, part of Hitachi Global Storage Technologies. At Virident, they solved the then perplexing data storage problem to help enterprises handle performance-intensive data center applications with PCIe-based enterprise flash storage solutions for virtualization, database, cloud computing and webscale applications.
Now this same team, complemented by a strong group of AI data specialists, is tackling the biggest data challenge that is so crucial to advancing Artificial Intelligence (AI) that will drive the next phase of human progress. AI is tackling self-driving cars, unattended retail, and smart cities, but will go well beyond that to transform the world. We simply call it The Autonomous World. It’s coming, and it has a massive Exascale-class data problem that Akridata helps you solve.
What is the Autonomous World?
Autonomy is the next phase of AI and one of the most significant endeavors in human history and the data problem associated with it represents a giant challenge – an avalanche of data generated at the Edge. At the center of the Autonomous World are self-driving cars, unattended retail stores, smart manufacturing plants and cities, and AI-enabled healthcare, among other significant innovations. These Edge devices will produce and consume most of the World’s data by 2025 – that’s only four years away! And the massive data generated at the Edge is becoming simply too large to be sent to the cloud or to be held locally.
The volume, variety, and velocity of this rich data – typically video and lidar data — is creating an ExaScale data challenge demanding a new decentralized data management approach with smart technology to support automated workflows.
Data-Centric AI
This new world relies on Edge data, AI models built with Deep Learning, and using new data from to continuously improve AI models. Because edge devices are scattered around the world and can be fixed or mobile, an avalanche of Edge data flows through resources from Edge to Core to Cloud – sometimes multi-Terabytes of rich data per day for just one self-driving test car. This Edge data must be ingested, filtered, tracked, explored, and be retrievable quickly and cost-effectively.
The Akridata team could see that algorithm development, deep learning, and cloud technologies were making rapid progress but decentralized edge, where almost all the data is generated and ultimately used, was a glaring gap. They knew that data will become the most critical part of AI and it needs a solution that is designed as a distributed platform. Not cloud-centric or edge-centric, but data-centric. And that’s exactly what has happened.
The realization that data has become the most critical part of AI now has led to a new phase in AI’s progress: Data-Centric AI.
Akridata sees Data-Centric AI getting built on a foundation that is designed for the end-to-end device-to-cloud or service-to-cloud spectrum. a decentralized platform that understands
This story posted by Datanami in Sept. 2021 cites core elements of this pervasive edge data problem.
Data today doesn’t exist behind firewalls or within on-premises locations. It is globally distributed in siloed databases, data warehouses, and more across on-premises, network edge, and multiple clouds. To unlock value, applications need access to that data to contextualize it and correlate it across different datasets.
According to this eWeek story also just published in Sept. 2021, the trifecta is Edge Computing, Edge Networking and Edge Data, citing that that third element really hasn’t been solved.
Until today. Meet Akridata, aptly named as Akri means Edge in Greek, and its Decentralized Data Platform that serves as the foundation for Data-Centric AI in massive new ways.
Foresight: Three Key Happenings in 2015
Our founding leaders saw this big data challenge coming in 2015, when they started to see the Autonomous World taking shape. Three significant happenings were falling into place:
They knew that a new data management system was going to be needed to support the rapidly evolving Autonomous World and its new and unique needs. They saw the challenge first in the auto industry with ADAS/AV initiatives, but also knew other industries with full-force streams of video data and autonomous devices at the Edge would all also have similar data management requirements.
They realized powering the Autonomous World requires locating the data precisely relevant to specific situations; finding the correct scene among tens of thousands of video clips; comparing different AI algorithms according to the data sets that trained them; and cataloging the data so that one can find and retrieve what’s needed wherever the data, or the autonomous device, may reside.
A new data management solution needed to be invented to handle the flood of data at the Edge and to smartly organize, store and allow access to the data in a decentralized manner across the Edge, the Core and the Cloud.
These new data management requirements are solved with the Akridata Edge Data Platform (link to the product blog post for more details) to expertly ingest, catalog, explore, and track AI data in a decentralized approach. The firm’s customers have varying use cases and Akridata has been proven to deliver 10-fold faster time-to-access the right data; four-fold increase in compute/storage efficiency; and two-fold productivity increase for data scientists.
A Culture of Solving Challenges
Our co-founders, Kumar and Vijay, lead Akridata with an open culture so team members feel free to challenge ideas — crucial to solving the world’s toughest problems. They understand technologies and markets and the stable force each has on the other. They saw a massive need – the white space — in the market and set out to fill it. They take stock in the Stockdale Paradox, a POV that centers on balancing the right amount of optimism with realism – hope for the best, while prepping for the worst.
With great vision, this team worked across the past few years to build and prove out the Akridata solution with a rigorous day-to-day approach, applying Agile software development method and a powerful conviction for its thesis – that Data Centric AI can be enabled with a decentralized data management solution.
Click here to schedule a consult and learn how Akridata can fuel your AI innovation today.