Question-6: A product recommendation system is a software programme that is meant to produce and deliver ideas for things or content that a particular user might want to buy or interact with. Typically, these recommendations are tailored to the user's interests and preferences. Your client has a web service that other e-commerce websites use to provide consumers with individualised product suggestions. In an effort to enhance the overall quality of the findings, the business has started some testing using a machine learning model hosted on Google Cloud Platform. In the field of machine learning, the term product recommendation refers to the job of proposing product(s) to a consumer based on the client's previous purchasing history. A machine learning model that makes suggestions to a user about products, pieces of information, or services that they would like purchasing or using is called a product recommender system. What steps should the end user take to optimise the performance of their model over time?
A. In order to investigate the performance of the model, provide the metrics about the Cloud Machine Learning Engine's performance to BigQuery from Stackdriver.
B. Develop a strategy for migrating the training of machine learning models from Cloud GPUs to Cloud TPUs, which provide more accurate results, and then put your plan into action.
C. Keep a watch on the announcements made by the Compute Engine to determine when new CPU architectures are available. As soon as they are, change the model to use those architectures for improved overall performance.
D. BigQuery should be utilized to keep a record of past suggestions and the outcomes of those recommendations so that they may be used as training data.

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