Part 2/13:
Maintaining performance in changing data environments: Since real-world data is dynamic, models in production need to adapt on the fly.
Identifying and correcting errors: Language models will inevitably make mistakes; recognizing and addressing these is vital for trustworthy AI.
Before delving into technical details, the speaker briefly revisits what constitutes a pre-trained model versus a fine-tuned model, setting the stage for more in-depth discussion.