1. How do they handle edge cases?
Data annotation partners play an important role in the success of your AI product. Unfortunately, most teams focus only on price and timeliness. The truth is, real project success depends on digging deeper. You have to ask the right questions about quality and expertise in order to achieve it. A good annotation partner supports your goals and protects your data.
Let’s look at the seven questions you must ask before making a decision.
Edge cases are uncommon or confusing data points. They might not follow typical patterns, but they can show up in actual use. If your annotation partner overlooks these edge cases or gives them the wrong labels, your model might fail. Ask your partner about their approach to unclear examples.
- Do they have a system to flag such cases for a closer look?
- Can they show you examples of how they dealt with edge cases in earlier projects?
A good partner will also have defined procedures to deal with and identify edge cases. This helps to ensure that your data remains reliable and accurate.
2. What domain expertise can they provide?
General annotation services usually struggle with sophisticated terms and context. For instance, annotating images for autonomous vehicles is quite different from annotating X-rays for hospitals. Your annotator’s partner should know your kind of data.
If your project is in healthcare, finance, or e-commerce, you must be familiar with that field. Ask them how they prepare their teams for special projects. Ensure they have done similar AI projects like yours in the past.
3. What is their annotator qualification process?
People doing the annotators must be skilled and trained. Ask your partner how they hire their staff. Do they test their skills before hiring? What training do they give before starting a job? How often do they review the workers’ performance? The best partners usually train their annotation teams. They also have checks to make sure the work stays good over time.
4. How adaptable is their annotation platform?
As you grow your project, your needs may change too. You could need to adjust labeling rules, add new categories, or tweak your workflows.
A rigid system will hold you up. Ask if their platform is simple to modify. Are they able to quickly apply changes when you need them? Are you able to test new guidelines without hesitation? This can save time and help you keep your project on track.
5. What integration options do they offer?
Moving data back and forth wastes time and leads to mistakes. Find out what tools and formats your partner can work with. Can their solution integrate with your store data or your machine learning platform? Do they provide APIs or integration options? A good partner will ensure that your systems function more effectively together and data sharing is easy and secure.
6. How do they measure and report quality?
Accuracy is important, but how do they verify it? You require clean quality checks. Do they utilize agreement scores, sampling, or review rounds? How do they report this information back to you? Can you see quality metrics at every step? A good partner will provide transparency. They’ll demonstrate where your data stands and how it gets better over time.
To conclude
The right annotation partner is very important for a successful project. You need a team that understands your data and keeps your project safe.
At Aipersonic, we’ve established our business on tackling these key drivers of the success of annotation projects. As one of the best AI training data services providers, our platform integrates domain expertise and enterprise-level security with speed and accuracy.
Looking for a new annotation partner? We invite you to evaluate us against these seven factors. Reach out to us to talk about your particular project requirements or begin with a small pilot to see our approach in action.