Check out our videos to learn about the latest developments and trends in data annotation.


For companies striving to unlock the full potential of machine learning, access to accurate and scalable datasets often represents a significant bottleneck. This is in part because many common approaches to data labeling come with tradeoffs between accuracy, cost, and time investment.


Concerned about Safety? Your Training Data could be Veering you off Course

There are many challenges on the way to building AV solutions and high quality labeled data is one of them. During this informative session, Renata will highlight the importance of building a robust automotive annotation quality strategy.


A day in the life of an ML engineer or a data scientist is not as glamorous as you might think; data-related tasks — from aggregating to labeling and augmenting data — can take up to 80% of their time. At Sama, we’ve helped hundreds of organizations overcome data challenges at every stage of the AI model lifecycle.


Recognizing and Overcoming Data Annotation Challenges for Enterprise Machine Learning

Access to timely, adequate volumes of high-quality labeled data is one of the biggest barriers to optimizing model performance and effectively productizing enterprise ML solutions. The good news is that as an increasing number of computer vision models make it into production, best practices are crystallizing.

High-Quality Training Data From Start to Scale.