When did you need to annotate data for machine learning?

Hey everyone! I’m Karyna from Label Your Data. Quick question — how many of you in ML have had hands-on experience with data annotation? In my work, I see supervised learning projects needing labeled datasets all the time. These labels act like a ground-truth reference, helping models learn to detect objects in real-time data. Some of my clients started out with public datasets (think Kaggle) but soon realized they needed something way more specific. That’s when they decided to collect their own field data and came to us for labeling. Others tried automated tools like Python scripts or even Segment Anything. But when it came down to spotting super specific objects, they needed a more customized approach and reached out to us. I think it's a very dynamic landscape, so I'd love to learn more about your experience going through data annotation.