From the course: AI Trust and Safety: Navigating the New Frontier
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Real-time monitoring for AI systems
From the course: AI Trust and Safety: Navigating the New Frontier
Real-time monitoring for AI systems
- In this chapter, we'll focus on keeping AI systems safe and reliable after deployment. This includes monitoring user behavior, incorporating feedback, and managing incidents effectively. AI doesn't stop needing attention after it's deployed. Let's explore how to keep your systems running smoothly and ethically. Log interactions. Think of it as your system's memory. By recording users' inputs and outputs, you can spot patterns, anomalies, or potential misuse. Detect anomalies. Use machine learning models to highlight behaviors that seem out of place. A little like having a digital watch doc. Track key metrics. Keep an eye on the essentials. Is your model accurate? Are false positives or negatives becoming a problem? Are different user groups seeing consistent results? Visualize data. Dashboards can turn raw numbers into clear trends, helping your team act fast when something feels off. Last, address data drift. Monitor the incoming data. If it changes significantly, your system might…
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