Improve an e-commerce recommendation ML model over time on GCP. What should you do?

To improve an ML model over time, it's crucial to continuously feed it new, relevant data. Persisting recommendation requests and their outcomes in BigQuery creates a valuable dataset of real-world interactions. This data can then be used to retrain the model, allowing it to learn from user behavior and adapt to changing preferences, thereby improving its accuracy and effectiveness. Exporting Cloud ML Engine performance metrics to BigQuery is useful for monitoring model health and efficiency, but it doesn't directly provide new training data to improve the model's predictions. Migrating from Cloud GPUs to Cloud TPUs might offer performance improvements for training speed, but it doesn't address the fundamental need for updated training data to enhance model accuracy. Monitoring Compute Engine CPU architecture updates is a general infrastructure concern and doesn't directly contribute to the ML model's learning or improvement process.