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You need to analyze the performance of AI models deployed in different regions, focusing on response accuracy, latency, and user satisfaction. The goal is to present regional differences in these metrics to guide future deployment strategies. Which visualization technique would best help compare these performance metrics?
In a binary classification problem with highly imbalanced classes, which algorithmic approach is most effective in improving model performance on the minority class without sacrificing the overall accuracy?
Your team is deploying a LLM for a customer service chatbot that must handle high concurrency and provide accurate responses within milliseconds. Which two actions would best improve scalability and performance?