What are the best practices for AI error detection?

What are the best practices for AI error detection?

The best practices for AI error detection include regular monitoring, implementing robust validation techniques, and using diverse datasets.

Can you explain what robust validation techniques are?

Robust validation techniques involve testing AI models against various scenarios to ensure they perform well under different conditions.

How often should I monitor my AI systems for errors?

It's recommended to monitor AI systems continuously, but at a minimum, you should conduct regular audits, such as weekly or monthly.

What types of datasets should I use for training?

Use diverse datasets that represent different scenarios and edge cases to improve the model's ability to detect errors.

Are there any tools you recommend for error detection?

Yes, tools like TensorFlow Model Analysis and MLflow can help in monitoring and detecting errors in AI models.