Google Professional Machine Learning Engineer
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Your client operates a large e-commerce platform specializing in sports goods, with a significant focus on scuba diving equipment. The business is subject to seasonal fluctuations and has accumulated extensive sales data from structured ERP systems and market trend databases. The client's primary goal is to forecast customer demand accurately to not only boost business growth but also to optimize logistics and inventory management processes. Given the need for a solution that is both managed and user-friendly on GCP, which two products would you recommend for developing these predictive models? (Choose two.)
Explanation:
BigQuery ML and Auto ML are the most suitable GCP products for this scenario due to their managed nature and ease of use, which align with the client's requirements for quick deployment and simplicity. BigQuery ML allows for the creation and execution of machine learning models using SQL queries, making it accessible for those familiar with SQL. Auto ML provides a no-code solution for training high-quality models tailored to specific business needs without requiring deep expertise in machine learning. KubeFlow and TFX, while powerful, are more suited for complex, custom machine learning workflows and require more setup and management, making them less ideal for the client's stated needs. Vertex AI, although a managed service, is more comprehensive and might be overkill for the client's specific requirements. For further reading, refer to the BigQuery ML documentation and Auto ML for time series forecasting.