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How to Organize Data Labeling for Machine Learning: Practical Approaches

In this contributed article, AI and computer vision enthusiast Melanie Johnson discusses how prior organization of data labeling for a machine learning project is key to success. Organizing data labeling for machine learning is not a one sitting job, yet a single error by a data labeler may cost you a fortune. Now, you probably wonder how do I get high-quality datasets without investing so much time and money?

Why Partnerships Beat Outsourcing In Data Labeling

In this contributed article, Mohammad Musa, Founder & CEO of Deepen AI, discusses how good data labeling leads to better results, whether it’s in autonomous cars, medical imaging, or any other industry where AI thrives. Done poorly, the entire system suffers. Inefficiencies and inaccuracies become inevitable, while major safety risks caused by poor labeling can derail an entire project.