From the course: Implement and Evaluate Cloud AI Solutions: Foundations of Responsible AI
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Iterative improvement of AI systems
From the course: Implement and Evaluate Cloud AI Solutions: Foundations of Responsible AI
Iterative improvement of AI systems
- Think of an AI model like a recipe you are perfecting over time. The first version might be decent, but with each iteration, adding a pinch of salt here, adjusting the cooking time there, it gets better. AI systems also thrive on iterative improvements to adapt to new data, evolving user needs, and changing environments. Let's dive into the strategies that keep AI models effective and up-to-date. Imagine launching a mobile app and never updating it. Over time, bugs emerge, new devices create compatibility issues, and user expectations change. AI models face similar challenges. Data distributions evolve, preferences shift, regulations change. Iterative improvement ensures AI models grow alongside these shifts, much like updating an app to stay relevant and functional. Think of retraining an AI model like refining a recipe based on new ingredients or feedback. Here is how retraining can work. Batch training: periodically updating the model with batches of new data, like perfecting a…
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