From the course: Implement and Evaluate Cloud AI Solutions: Foundations of Responsible AI
Join today to access over 24,900 courses taught by industry experts.
Continuous monitoring for AI systems
From the course: Implement and Evaluate Cloud AI Solutions: Foundations of Responsible AI
Continuous monitoring for AI systems
- Think of an AI model as a garden. When you plant seeds, you don't just walk away and expect a flourishing garden in a few months. You need to water it, pull out the weeds, and sometimes add fertilizers to help it grow. AI models need the same kind of attention through continuous monitoring. Without it, you could end up with a model that's overgrown with inaccuracies and weeds of bias. Imagine running a cafe. You wouldn't set up your espresso machine once and expect perfect coffee forever, would you? You would regularly check its temperature, clean the filters, and ensure beans are fresh. In AI, continuous monitoring works much the same way. Once deployed, models can experience drift. Their accuracy and performance may degrade over time as input data or external conditions change. For example, a model predicting customer churn might struggle if consumer behavior shifts due to an economic downturn. A weather prediction model might weaken with unusual climate patterns. Continuous…
Practice while you learn with exercise files
Download the files the instructor uses to teach the course. Follow along and learn by watching, listening and practicing.
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.