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Answer: Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
The correct answer is A. Configuring Kubeflow Pipelines allows for an end-to-end architecture that can automate the entire process of training and deploying the model. Kubeflow Pipelines follow Google-recommended best practices for building and managing machine learning workflows. They provide robust solutions for scheduling, executing, and monitoring machine learning workflows, which ensures that the model can be retrained and redeployed automatically every month. Other options either do not provide the end-to-end solution needed or do not align with the best practices set by Google.
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You are an engineer at a public transportation company responsible for improving the user experience for your customers by providing real-time delay time estimates for multiple transportation routes. The predictions generated by the model must be served directly to users via a mobile app in real time. Considering that different seasons and population increases affect route data relevance, you must retrain the model every month to maintain accuracy. Following Google-recommended best practices for machine learning workflows, how should you design the end-to-end architecture for training and deploying your predictive model?
A
Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
B
Use a model trained and deployed on BigQuery ML, and trigger retraining with the scheduled query feature in BigQuery.
C
Write a Cloud Functions script that launches a training and deploying job on AI Platform that is triggered by Cloud Scheduler.
D
Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model.