Webinars & Videos
Check out our recent webinars and videos to learn about the latest development and trends in data annotation.
How to Optimize Data Quality for Better ML Model Performance
Your ML model’s success requires more than data. It needs a comprehensive approach to quality control that engages with an annotation partner who’s well-versed in the latest research-driven practices, a smart quality strategy that balances annotation precision with practical needs, and the agility to recognize and propose process enhancements in real-time.
2023 Trends in Autonomous Driving
As 2023 kicks into gear, the promises and challenges of autonomous vehicle development are evermore present. What trends and major predictions can we expect for this year?
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.
How to pick the best data labeling solution, and what you should consider when choosing a partner?
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.
Frequently Asked Data Labeling Questions – Sama at Ai4 2022
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.