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- AI Data
Five common data annotation challenges and how to solve them
Data annotation can create bottlenecks in AI and machine learning projects. Learn how to improve data quality and overcome data annotation challenges.
- AI Data
Quality assurance best practices for AI training data
Training AI systems demands observing data quality best practices. Learn more about AI data quality control in this primer.
- AI DataAutomotive
Does data hold the key to autonomous vehicle safety?
Experts believe autonomous vehicles will substantially improve road safety. Discover how large, diverse and accurate datasets are enabling this vision.
- AI Data
Data labeling fundamentals for machine learning
Machine learning demands massive amounts of clean, structured, labeled data. Learn the fundamentals of data labeling for successful AI training.
- AI Data
The expanding frontiers of computer vision in the metaverse
Learn how computer vision will play a vital role in transforming the metaverse into a commercially accessible reality for brands across industries.
- AI Data
The contemporary trifecta of data-centric AI
Data-centric AI is driving the success of current machine learning initiatives. Learn about the leading role of data in the AI lifecycle and the three key factors powering the data-centric approach.
- AI Data
Autonomous technology: Thinking beyond automobiles
Learn what autonomous technology is, popular use cases beyond autonomous vehicles, its many benefits and the challenges faced by organizations today.
- AI DataAutomotive
Driver monitoring systems: Needs, regulations, popular use cases and trends
Driver monitoring systems (DMS) are proving to be effective in helping prevent accidents due to driver drowsiness or inattention. Learn all about these AI-backed systems.
- AI Data
Closing the gender data gap in AI
What is the gender data gap in artificial intelligence (AI) and what can companies do to ensure more inclusive AI data practices? Discover key insights and learnings.
- AI Data
A guide to building training data for computer vision models
Explore what is involved in the collection and labeling of training data that is used to build computer vision models.
- AI Data
Automation: The antidote to overcoming data labeling inefficiencies
Learn how machine learning assisted automation speeds up data labeling and improves dataset accuracies for computer vision teams.
- AI DataAutomotive
Video annotation for computer vision
Video annotation enriches video data by adding object information tags across every frame. Discover different video annotations, their popular computer vision applications and the benefits of video labeling automation.
