Google Professional Machine Learning Engineer
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You are tasked with developing a machine learning solution for a retail company that wants to predict customer churn based on historical transaction data stored in BigQuery. The solution must support exploratory data analysis, feature selection, model building, training, hyperparameter tuning, and serving, all without requiring manual coding. The company emphasizes the importance of scalability, cost-efficiency, and the ability to repeat the classification process across multiple datasets with minimal setup. Given these requirements, which of the following approaches is the most efficient? Choose the best option.
You are tasked with developing a machine learning solution for a retail company that wants to predict customer churn based on historical transaction data stored in BigQuery. The solution must support exploratory data analysis, feature selection, model building, training, hyperparameter tuning, and serving, all without requiring manual coding. The company emphasizes the importance of scalability, cost-efficiency, and the ability to repeat the classification process across multiple datasets with minimal setup. Given these requirements, which of the following approaches is the most efficient? Choose the best option.
Explanation:
AutoML Tables is the most efficient approach for the given scenario as it fully automates the machine learning workflow without the need for coding, aligning with the company's requirements for scalability and cost-efficiency. However, combining AutoML Tables with BigQuery ML (option E) could also be considered for leveraging AutoML's ease of use for initial exploration and BigQuery ML's scalability for training and serving, making it a viable second option for teams looking to balance ease of use with scalability.