As new uses for cameras and the visual data created become available, the need for tools to find the most valuable subsets becomes essential.

The challenge Imagine you’ve just been given a new batch of 10,000 images or hours of video and you need to find only a small portion of relevant images. How would you go about isolating that subset of the data? In many cases, we receive a batch of visual data,

ECCV 2022 was a truly remarkable event. With two full days of incredible workshops and tutorials, followed by three additional days of the main conference, Computer Vision was celebrated in every corner. During the conference, giants like Amazon, Google and Meta, giants-in-the-making like Akriadata, and a whole host of awesome

nuScenes is a public dataset for autonomous driving. In this post, we’ll break down how Akridata Data Explorer makes exploration easy.

The autonomous world is rapidly becoming one of the most significant developments in human history. From self-driving cars to automated retail stores and warehouses, AI and machine learning have seamlessly integrated into our daily lives. The next frontier in AI is to push machines toward even greater autonomy, and achieving

Class imbalance-in-visual data sets is an all too common problem in real-world applications that use machine learning and AI. Many applications of computer vision suffer from imbalanced class distribution, including fraud detection, anomaly detection, medical diagnosis, oil spillage detection, facial recognition, and more. The consequences of imbalanced class data can

Explore how Akridata Data Explorer enhances visual data and AI modeling. Learn about its powerful features for dataset curation and efficient AI development.

GTC 2022 is Just Around the Corner! Next week, Nvidia’s GTC Conference and Training will be firing up with some of the foremost in AI across numerous sectors, and we’re looking forward to connecting, engaging, informing, and learning with all of you from March 21-24. This year, Akridata’s Senior Director

The December edition of Tech Briefs from SAE International featured a short paper from Akridata on how a Data Supply Chain is a critical part of our emerging AI-infused world.  This is best seen in the transportation industry where accelerating ADAS/AV development demands a cohesive modern Edge-to-Cloud perspective focused on data. As

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

AI guru and one of Time magazine’s 100 Most Influential People in 2012, Andrew Ng recently launched a campaign to drive the adoption of “Data-Centric AI.” Why? Because at this point in the evolution of AI, the success and the future of real-world AI applications hinges on the quality of data. While ML/AI hardware infrastructure and

By Kunal Vasavada Recently, I visited a mattress store to find one for my toddler son. This was a rather large store with so many brands, comfort styles, price tags and more. Within a few minutes of browsing around, testing a few mattresses by hand to check for firmness, reading

Launching Akridata required many research and brainstorming sessions with the founding team. Now Sanjay Pichaiah, Vice President of New Business, is sharing insights, resources, and interesting finds into the complex data problems facing the AI market over at Medium. “What stood the test of our shredding machine, was the problems

The Autonomous World represents one of the most significant advancements in human history. As we move closer to a fully autonomous future, we’re witnessing its transformative impact across various sectors, including: Self-Driving Cars Shipping & Deliveries Unattended Retail Hospitals Warehouses Factories Farms Construction Sites At the heart of this revolution