End-to-end platform enhancing edge-core-cloud data flows launched by Akridata

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Akridata, which specializes in data-centric AI, has launched its Edge Data Platform, which creates and manages smart data pipelines and AI workflows spanning Edge-Core-Cloud resources.

The company states that its latest software solves the problems that emerge when streams of rich data from physically scattered edge devices create an avalanche of data. This volume of information can prove impossible to organize, filter, access and process. It is now common for organizations to collect tens of terabytes of data per day from a single autonomous device.

To address this challenge, Akridata says it is able to provide a decentralized structure and scalable process to deliver curated, consistent and relevant AI data sets.

“The diverse requirements of ADAS/AV (advanced driver assisted systems/autonomous vehicle) necessitate many elements including deep learning, cloud deployment and in-vehicle services, among others. What ties all these together is data and a massive data challenge,” said Kishore Jonnalagedda, director of engineering, Toyota Motor North America.

“Akridata brings us a comprehensive and novel solution that drives efficiencies, lowers the cost and accelerates our team’s work toward our objectives. 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 best-in-class ADAS/AV solutions and providing mobility for all.”

The company notes that its solution is optimized for advanced AI workloads, providing built-in capabilities for AI-oriented data organization, transformation, and filtering tasks. It allows tracing and tracking of data from inception to inference, it enables traceable AI and broadly complements industry efforts toward explainable AI (XAI). This makes it possible to track the evolution of AI models and link the behavior of AI models in the field to the data sets that contributed to the specific model used by a specific device or service.

For autonomous driving applications, streams of rich data – such as videos and lidar data – generated by fixed or mobile edge devices must be organized, filtered, tracked and processed across edge-core-cloud resources. Massive amounts of data are being generated at the edge by these devices. For example, a self-driving car in its test phase can generate as much as a terabyte of data in a single day. It is predicted that by 2025, three quarters of the 175 zettabytes of new data generated will come from the edge.

“Akridata is making the autonomous world possible by providing the last 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,” noted Kumar Ganapathy, co-founder and CEO of Akridata. “The future of AI is all about data, and our focus on AI data since inception gives Akridata a first-mover advantage. We are pleased to launch the first product in the Data-Centric AI Category, and to be working with a range of customers including industry leaders like Toyota Motor Company North America.”

The distributed nature of the Edge Data Platform apparently helps optimize the processing, storage and movement of data across the edge, the core and the cloud and is claimed to deliver 10 times faster time-to-access data and four times more efficient usage of compute and storage.

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Lawrence has been covering engineering subjects – with a focus on motorsport technology – since 2007 and has edited and contributed to a variety of international titles. Currently, he is responsible for content across UKI Media & Events' portfolio of websites while also writing for the company's print titles.

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