PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #43
PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #43: Real Exam Question with Answer & Explanation
The correct answer is A: Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your. Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning (ML) workflows based on Docker containers. It allows you to define your ML workflow as a sequence of steps, and then schedule it to run on Kubernetes. This makes it a good choice for s
Question
You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?
Options
- AConfigure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your
- BUse a model trained and deployed on BigQuery ML, and trigger retraining with the scheduled
- CWrite a Cloud Functions script that launches a training and deploying job on AI Platform that is
- DUse Cloud Composer to programmatically schedule a Dataflow job that executes the workflow
Explanation
Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning (ML) workflows based on Docker containers. It allows you to define your ML workflow as a sequence of steps, and then schedule it to run on Kubernetes. This makes it a good choice for scheduling the retraining of your model, as it can be easily scaled to handle large datasets and complex workflows.
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