From a Million Images to Two - Reinventing Computer Vision
Introduction
Processing and annotating data against thousands of images per object is a multi-million dollar industry; creating AI engines requires teams of data engineers and years of build time. All of this leads to enormous barriers to entry for even the largest enterprise customers.
What if annotation and labeling were automatic, and mini-engines were built for specific business use cases? Accuracy levels skyrocket, while costs are massively reduced, and only a handful of images are required. Amnicisent's CEO, Suresh Yamanchili explains in this edition of the Genius Scale Podcast.
Key takeaways:
- Processing data and collecting data is expensive and time intensive. Year over year, the quantity is heavier and more unrealistic for us to handle. Computer vision closes the gap with automation.
- Data sharing isn't a pre-requisite for computer vision or model making; in fact it makes application more difficult, more noisy, and harder to pinpoint real world application.
- All engines don't unilaterally work; they are nuanced and not necessarily built for specific real world use cases. Dedicated engines are creating utility and real world business insights and change.
"What if you didn't have to share your data? Your use case is original, your business is original, and 99% of what's out there [other business data] is irrelevant to what you are doing. Real world models, that are ready in weeks, with a handful of images and automatic annotation are what will change real business." - Suresh Yamanchilli, Amniscient's CEO and Founder
Amniscient's platform and engine were built from the ground up to specifically address the challenges that operators deal with every day, saving customers millions in data and annotation processing, as well as upload time, without sacrificing accuracy. There are opportunities to augment operational speed, product quality, and revenue at every inflection point in the supply chain. Find out more or talk to one of our experts.
