How to Monitor the Quality of an AI After Go-live?

Introduction

Monitoring the quality of artificial intelligence (AI) after go-live is a critical process to ensure that the AI continues to operate effectively and reliably. This involves various methods and approaches aimed at continuously assessing and, if necessary, adjusting the AI's performance.

Continuous Monitoring

A central aspect of quality assurance is the continuous monitoring of AI performance. Various metrics such as accuracy, precision, recall, and F1-score are regularly collected and analyzed. These metrics provide insights into how well the AI is performing its tasks and whether it meets expectations.

Regular Evaluations

In addition to monitoring, regular evaluations should be conducted. These evaluations can take the form of scheduled audits or reviews, where the AI models are assessed for their performance. It is important to test the models not only on the original training data but also on new, real-world data to ensure that the AI functions reliably under changing conditions.

Feedback Loops

Another important aspect is the implementation of feedback loops. User feedback can provide valuable information about how the AI is perceived in practice and where improvements may be needed. This feedback should be systematically collected and analyzed to make targeted adjustments to the AI models.

Adaptation to New Data

The performance of an AI can be affected by changing data landscapes or new requirements. Therefore, it is crucial to regularly test the AI with new datasets and retrain it if necessary. This helps maintain the relevance and accuracy of the AI models and ensures that they continue to meet future demands.

Conclusion

Quality assurance of an AI after go-live is an ongoing process that includes continuous monitoring, regular evaluations, and consideration of user feedback. Through these measures, the performance of the AI can be sustainably secured and improved.