How can I detect data poisoning in my AI models?

How can I detect data poisoning in my AI models?

Detecting data poisoning involves monitoring your training data for anomalies and inconsistencies. Have you noticed any unusual patterns?

Yes, I've seen some unexpected results in my model's predictions.

That could be a sign of data poisoning. You might want to analyze the data distribution and check for outliers.

What tools can I use to analyze the data?

You can use tools like TensorFlow Data Validation or PyCaret for data profiling and anomaly detection.

Are there any best practices to prevent data poisoning?

Yes, regularly auditing your data, using robust validation techniques, and employing anomaly detection algorithms can help.