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Answer: Configure Kubeflow Pipelines to manage the multi-step workflow from data preprocessing, model training on AI Platform, to deployment, facilitating easy retraining and adherence to best practices.
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.
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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.
A
Use Cloud Composer to orchestrate a Dataflow job that processes data, trains the model on AI Platform, and deploys it for real-time predictions, ensuring scalability and cost-efficiency.
B
Configure Kubeflow Pipelines to manage the multi-step workflow from data preprocessing, model training on AI Platform, to deployment, facilitating easy retraining and adherence to best practices.
C
Develop a Cloud Functions script that triggers a training and deployment pipeline on AI Platform via Cloud Scheduler, suitable for lightweight and event-driven retraining scenarios.
D
Implement a solution using BigQuery ML for training and deploying the model, leveraging its programmed query feature for monthly retraining, ideal for teams with strong SQL expertise.
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