Resource Data

Akridata Edge Data Platform Accelerates Access to Relevant AI Data


Akridata launched Akridata Edge Data Platform, which creates and manages intelligent data pipelines and AI workflows spanning Edge-Core-Cloud resources.

Akridata software solves problems that arise when rich data streams from physically dispersed Edge devices create an avalanche of data that cannot be organized, filtered, accessed, and processed. It’s now common for organizations to collect tens of terabytes of data per day from a single stand-alone device.

With the first AI infrastructure platform in the Data-Centric AI category, Akridata provides a decentralized structure and scalable process to deliver organized, consistent and relevant AI datasets. Akridata was formed in 2018 to solve the AI ​​data problem. Solving this Exascale class problem is a requirement for moving AI from experimentation to production in the real world. From automobiles to transportation, retail and healthcare, this need is a major barrier preventing AI-enabled products from reaching the market.

“The diverse requirements of ADAS / AV (Advanced Driver Assistance Systems / Autonomous Vehicle) require many elements, including deep learning, cloud deployment and in-vehicle services, among others. What ties it all together is data and a huge data challenge, ”said Kishore Jonnalagedda, director of engineering, Toyota Motor Company North America.

“Akridata brings us a complete and innovative solution that improves efficiency, reduces costs and speeds up our team’s work towards our goals. We will gain immediate leverage by automating data pipelines from edge locations to the cloud, allowing our data science and product development teams to focus on what matters most: delivering the best ADAS solutions. / AV and ensure mobility for all. ”

Akridata’s solution is optimized for advanced AI workloads, providing built-in capabilities for AI-driven data organization, transformation and filtering tasks. It enables the tracing and tracking of data from creation to inference, it enables traceable AI and largely complements industry efforts towards Explainable AI (XAI). This allows the evolution of AI models to be tracked and the behavior of AI models in the field to be linked to the datasets that have contributed to the specific model used by a specific device or service.

“Akridata makes the autonomous world possible by providing the final piece of the puzzle: an integrated Edge-Core-Cloud data platform that solves the data problem at the heart of all real-world AI systems,” Kumar said. Ganapathy, co-founder and CEO of Akridata. “The future of AI is all about data, and our focus on AI data since its inception gives Akridata a leading edge. We are excited to launch the first infrastructure product in the Data-Centric AI category and to work with a range of customers, including industry leaders like Toyota Motor Company North America. ”

“To thrive in the emerging global IT infrastructure, businesses and other organizations will need to leverage heterogeneous data from highly distributed sources ranging from edge devices to powerful computers in clouds and data centers,” said Steve Conway, Advisor principal at Hyperion Research. “Akridata is well positioned to benefit from the strong growth that Hyperion Research expects in this emerging data-driven market. ”

Akridata’s innovative new solution enables the integration of deep learning with inference, edge commerce, data governance and enterprise applications. It was specially developed to address the Exascale-class data challenges of delivering advanced AI, autonomous devices, and unattended services.

“Advanced AI models are increasingly created and executed in the cloud, but require good quality data from edge devices,” said Jon Jones, Director – Go-to-Market for AI / ML , EC2 and Autonomous Vehicles, AWS. “The high volume and complex nature of this data has created a new exascale class problem. Akridata solves the problem by managing smart pipelines for AI data ingestion, filtering, curation, tracking, and staging. ”

AI data complexity

The autonomous world depends on continuous deep learning using large volumes of complex AI data sets. Rich data streams – such as video and lidar data – generated by fixed or mobile edge devices must be organized, filtered, tracked, and processed on Edge-Core-Cloud resources.

Massive amounts of data are being generated at the edge by these devices. For example, an autonomous car in the test phase can generate several terabytes of data in a single day. By 2025, 75% of the 175 zettabytes of new data generated will come from the Edge, according to industry experts.

“Particularly for new workloads such as AD / ADAS development, HPC / IA workflows are increasingly facing an Exascale-class data challenge. The continuous flow of data from intelligent edge devices to the cloud creates a significant demand for data retention as it is absolutely necessary for subsequent AI and software development and validation pipelines, ”said Kurt Niebuhr, Director Main Program, HPC / AI Ecosystem and Workload Incubation, at Microsoft Azure. “Data-centric AI solutions like Akridata’s Edge data platform address this need and help customers match the sophistication of their analytics and models with relevant, high-quality data.

The Akridata Edge intelligent data platform is distributed and helps optimize the processing, storage and movement of data across the Edge, Core and Cloud. The Akridata platform is available now and has proven to deliver faster access time to the right data, more efficient use of compute and storage, and better productivity for data scientists and learning engineers. Automatique. With Akridata’s solution, real-world AI-powered products can come to life.



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