Buyer’s Guide / eBook
Buyer’s Guide: Data Annotation / Labeling
So, you want to start a new AI/ML initiative and are realizing that finding good data will be one of the more challenging aspects of your operation. The output of your AI/ML model is only as good as the data you use to train it – so the expertise you apply to data aggregation, annotation, and labeling is of critical importance.
Buyer’s Guide: High-quality AI Training Data
In the world of artificial intelligence and machine learning, data training is inevitable. This is the process that makes machine learning modules accurate, efficient, and fully functional. The guide explores in detail what AI training data is, types of training data, training data quality, data collection & licensing, and more.
Buyer’s Guide: Image Annotation for CV
Computer vision is all about making sense of the visual world to train computer vision applications. Its success completely boils down to what we call image annotation – the fundamental process behind the technology that makes machines make intelligent decisions and this is exactly what we are about to discuss and explore.
Buyer’s Guide: AI Data Collection
Machines don’t have a mind of their own. They are devoid of opinions, facts, and capabilities such as reasoning, cognition, and more. To turn them into powerful mediums, you need algorithms that are developed based on data. Data that is relevant, contextual, and recent. The process of collecting such data for machines is called AI data collection.
Buyer’s Guide: Video Annotation and Labeling
It is a fairly common saying we’ve all heard. that a picture could say a thousand words, just imagine what a video could be saying? A million things, perhaps. None of the ground-breaking applications we’ve been promised, such as driverless cars or intelligent retail check-outs, is possible without video annotation.
The Key to Overcoming AI Development Obstacles
There is indeed an incredible amount of data being generated every day: 2.5 quintillion bytes, according to Social Media Today. But that doesn’t mean it’s all worthy of training your algorithm. Some data is incomplete, some is low-quality, and some is just plain inaccurate, so using any of this faulty information will result in the same traits out of your (expensive) AI data innovation.