10/10/2019 | News release | Distributed by Public on 10/10/2019 08:24
Data annotation (also referred to as data labeling) is quite critical to ensuring your AI and machine learning projects can scale. It provides that initial setup for training a machine learning model with what it needs to understand and how to discriminate against various inputs to come up with accurate outputs.
There are many different types of data annotation modalities, depending on what kind of form the data is in. It can range from image and video annotation, text categorization, semantic annotation, and content categorization.
The vast majority of problems in which AI models are being built to address them can fit into one (or many) of the below annotation tasks:
We know having access to data is quite valuable, but having access to data with a learnable 'signal' consistently added at a massive scale is the biggest competitive advantage nowadays. That's the power of data annotation.