Google Professional Machine Learning Engineer
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As a professional working for a public transportation company, you are tasked with building a predictive model to estimate delay times for various routes. These predictions will be delivered to users in real-time via an app, and the model requires monthly retraining to account for seasonal and population changes. The solution must be cost-effective, scalable, and comply with data privacy regulations. According to Google's recommended best practices, which of the following options provides the BEST end-to-end architecture for this model? Choose the best option.
Explanation:
Kubeflow Pipelines is the best choice as it provides a comprehensive, scalable, and portable solution for managing the entire machine learning workflow. It supports data preprocessing, model training, hyperparameter tuning, and deployment, aligning with Google's best practices. Its integration with Kubernetes ensures scalability, and the pipeline's configurability facilitates monthly retraining. This approach is cost-effective, complies with data privacy regulations, and is designed for complex workflows like the one described.