Whether it’s NLP, CV, or speech recognition, one thing remains constant — AI models are only as good as the data they’re trained on. And at the heart of high-quality data? Thoughtfully executed data annotation.
In our recent experience working on multi-domain AI projects (ranging from object detection in manufacturing to sentiment analysis in ecommerce), we noticed that the accuracy, fairness, and efficiency of models dramatically improved when annotation was:
- Domain-specific
- Human-guided (with automation support)
- Built on consistent labeling frameworks
Here’s what we covered in our latest blog:
- When to use human vs. automated annotation
- Key annotation types for industrial AI (image, text, audio)
- Sector-specific use cases — from smart cities to supply chain
- Why outsourcing annotation often yields better model performance
Full breakdown here:
The Critical Role of Data Annotation in AI/ML
What are your thoughts?
- How do you manage annotation at scale without compromising quality?
- Have you seen measurable gains in model accuracy after relabeling or refining datasets?
Would love to hear about your experiences — let’s exchange best practices.