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Label Studio Survey Highlights Changing Investments and Technology Choices with the Shift from Model-Centric to Data-Centric AI 

Data science teams are shifting their focus from model development to dataset development in order to deliver Machine Learning (ML) and Artificial Intelligence (AI) initiatives that are more performant, differentiated and aligned with business goals. This and other findings are available in the first Label Studio Community Survey, where data scientists, ML engineers and researchers from the global open source community shared insights into the state of ML and AI. 

What to Avoid When Solving Multilabel Classification Problems

In this contributed article, April Miller, a senior IT and cybersecurity writer for ReHack Magazine, suggests that If you are working with a model with a multilabel classification problem, there is a likely chance you will run into something in need of fixing. Here are a few common issues you may encounter and what to avoid when solving them.

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.