What are the best practices for labeled data in AI training?

What are the best practices for labeled data in AI training?

The best practices include ensuring data quality, consistency, and diversity, as well as proper annotation guidelines.

How can I ensure the quality of my labeled data?

You can ensure quality by using multiple annotators, conducting regular audits, and providing clear instructions.

What tools can I use for labeling data?

Some popular tools include Labelbox, Prodigy, and Amazon SageMaker Ground Truth.

Is there a standard format for labeled data?

Yes, common formats include CSV, JSON, and XML, depending on the type of data and the model requirements.

How do I handle imbalanced datasets?

You can handle imbalanced datasets by using techniques like oversampling, undersampling, or synthetic data generation.