Your customer runs a web service used by e-commerce sites to offer product recommendations to users. The company has begun experimenting with a machine learning model on Google Cloud Platform to improve the quality of results. What should the customer do to improve their model's results over time?

To improve a machine learning model's results over time, it's crucial to provide it with more relevant and diverse training data. Saving a history of recommendations and their actual outcomes in BigQuery creates a valuable dataset that can be used to retrain and refine the model. This feedback loop allows the model to learn from its past performance, identifying patterns that lead to better recommendations. Exporting performance metrics to BigQuery (option 1) is useful for monitoring efficiency, but it doesn't directly improve the model's predictive accuracy. Moving from GPUs to TPUs (option 2) or to newer CPU architectures (option 3) can improve training speed or inference performance, but these hardware changes alone do not inherently improve the model's intelligence or accuracy without better data. The core of model improvement lies in the quality and quantity of its training data.