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Throughout the pandemic, “supply chain woes” have been blamed for everything from empty store shelves and skyrocketing consumer prices to a more recent surplus in inventory at big-box stores. All of this has many experts unsure about how to characterize the current state of the U.S. economy.
Machine vision was really born on the assembly line, designed as a system of existing technologies and machinery that, combined, “watch” the production process and recognize when flaws occur.
Concession and retail sales are a particular area where vision AI can make a huge impact for sports fans and other stadium patrons. With vision AI technology and a rich stream of visual data, spectators can expect a next-generation experience.
Deep learning sits within the machine learning subset of AI technologies. Machine learning systems are designed to educate themselves and adapt with or without human intervention. ML systems attempt to learn the same way humans do, through trial and error. Targeted ads, recommended products, and predictive search terms are all the result of successful machine learning.
Organizations can’t serve their customers with maximum efficacy unless they’ve got a deep repository of insights into customer behavior and preferences. Custom-built computer vision models like the ones Plainsight develops and deploys can help, empowering businesses to see more and make visual data (like video footage) into a driver of business innovation.
It was only a matter of time before the stats-obsessed sport and computer vision joined forces to create an unprecedented understanding of America’s favorite game.
Taking the first step toward more data-centric AI is as simple as recognizing the immense value your data holds and the value inherent in curating it carefully. But centralizing and systematizing your approach to AI isn’t enough—nor is it enough to focus on updating and testing the models you deploy at the expense of data quality. Even better than a data-centric approach to AI is a data-driven approach, one that focuses on both the quality of the data collected and the efficacy of the models this data is used to train.
Plainsight Deepens Relationship with Google Cloud to Empower Enterprises with Production-ready Computer Vision Solutions
Google Cloud customers can now centralize complete, end-to-end control and management of their vision AI workflows with fully-integrated capabilities in one intuitive interface and unmatched Google Cloud scalability and security for reliable and consistent vision AI solutions.
Data Annotation for Computer Vision
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