How can you determine if a data labeling service will deliver quality work? It starts with their vetting, hiring, and training processes.
CloudFactory Blog
People have unconscious biases that affect hiring decisions. People also can hard-code their biases into an AI system. Humans in the loop can help.
People are involved in everything from training and testing algorithms to labeling data, conducting quality control, and monitoring automation.
Humans play a critical role throughout the AI lifecycle, from data cleaning and labeling to quality control and automation monitoring.
Developing ML models requires a lot of data and skilled people to work with it. Here’s our HITL approach for machine learning model development.
We are excited to announce a new offering that bundles our professionally managed workforce with a market-leading annotation platform for one price.
Autonomous vehicles and AI driver safety tools aren’t affordable for all. Driver Technologies made a free innovative app and model training database.
Autonomous vehicles require continuous training to ensure they operate safely. Here’s how active learning can improve that process.
Autonomous vehicles are among the most data-hungry machines the world’s ever seen. Here’s a look at data collection techniques and challenges.
From localized bias to difficulties annotating video and radar data, here are some of the biggest challenges facing the future of autonomous vehicles.
In the Rise of Autonomous Vehicles the world of transportation and logistics is rapidly changing. Here are 3 AV innovators you should know about.
Training a car to drive itself is a heavy lift. Here’s how the process of Visual Data Collection for Autonomous Cars works and how it's used by people.
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.
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.
