What are the best practices for AI quality assurance?

What are the best practices for AI quality assurance?

Best practices for AI QA include thorough testing, continuous monitoring, and ensuring data quality.

Can you elaborate on the importance of data quality?

Data quality is crucial as it directly impacts the AI model's performance and reliability.

What testing methods should be used for AI models?

Common methods include unit testing, integration testing, and performance testing to ensure robustness.

How often should AI models be monitored after deployment?

AI models should be monitored continuously to catch any drift in performance and maintain accuracy.

What tools can assist in AI quality assurance?

Tools like TensorFlow Model Analysis, MLflow, and DataRobot can help streamline AI QA processes.