Can AI help us in predicting the future with computer vision? Here's how computer vision can use today’s data to model tomorrow’s outcomes.
CloudFactory Blog
Image annotation is an important task when training a computer vision model. Here are common misconceptions about image annotation for computer vision.
Quality data is the lifeblood of great computer vision applications. Here are 6 best practices for creating your own custom data sets for computer vision.
Great computer vision applications require a lot of quality visual data. Here's how you can acquire quality data sets for computer vision applications.
Agriculture data is complex. Annotating agtech data often requires help from agronomists. We help Hummingbird Tech overcome that AI product development hurdle.
AI is transforming healthcare; it arms practitioners to make better decisions & fewer errors. Here’s how image annotation in Medical AI makes it possible.
Images and videos are both means to an end to annotate visual data. Each may have its own unique process but in the end individual frames are being annotated on a meta data level. ...
Image annotation holds great promise for industries that rely heavily on visual data. Here are 3 of the biggest opportunities for image annotation.
After a decade of data work, we’ve learned AI development requires a winning combination of people, technology, and processes. Don’t overlook one important step before beginning ...
Your training data operations are like assembly lines: data is your raw material, and you have to get it through production steps to structure it for AI. You need skilled people ...
Why is artificial intelligence just now taking off? These three forces are fueling AI techniques to build real-life applications.
Some believe cloud robotics will be the future of AI and the next big disruptor in business.
Humans are the key to developing the datasets and algorithms required to train intelligent virtual assistants so they can mimic human intelligence.
Machines see the world through bounding boxes in order to make visual data meaningful, and that work can only be done by humans.
Augmented reality is here, in the workplace and in everyday life. For AR to truly succeed, it will require a heavy dose of imagination and a lot of data.
