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Indoor Robotics utilizes advanced robotics technology to revolutionize indoor security and monitoring, effectively tackling these challenges.
Check out our recent webinars and videos to learn about the latest development and trends in data annotation.
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Jason Corso (Voxel51), Duncan Curtis (Sama), and Rob Stevenson (How AI Happens podcast) discuss Sama’s 2023 ML Pulse Report on Generative AI, model production confidence, and overcoming the biggest challenges the industry faces as we move into 2024.
Duncan Curtis (SVP, AI Product and Technology) reviews three of the most impactful pitfalls when building ML models—and what you can do to avoid them.
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?
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.
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.
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.
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.
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.
Sama provides ML Professionals and AI team Leads with an indispensable solution for Computer Vision data labeling.
