Topic 1 Question 8
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?
Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
Use a model trained and deployed on BigQuery ML, and trigger retraining with the scheduled query feature in BigQuery.
Write a Cloud Functions script that launches a training and deploying job on AI Platform that is triggered by Cloud Scheduler.
Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model.
ユーザの投票
コメント(12)
Answer: A A. Kubeflow Pipelines can form an end-to-end architecture (https://www.kubeflow.org/docs/components/pipelines/overview/pipelines-overview/) and deploy models. B. BigQuery ML can't offer an end-to-end architecture because it must use another tool, like AI Platform, for serving models at the end of the process (https://cloud.google.com/bigquery-ml/docs/export-model-tutorial#online_deployment_and_serving). C. Cloud Scheduler can trigger the first step in a pipeline, but then some orchestrator is needed to continue the remaining steps. Besides, having Cloud Scheduler alone can't ensure failure handling during pipeline execution. D. A Dataflow job can't deploy models, it must use AI Platform at the end instead.
👍 29Paul_Dirac2021/06/22the answer is D. found similar explaination in this course. open for discussion. I found B could also work, but the question asked for end-to end, thus I choose D in stead of B https://www.coursera.org/lecture/ml-pipelines-google-cloud/what-is-cloud-composer-CuXTQ
👍 10gcp2021go2021/06/05To automate this model-building process, you will orchestrate the pipeline using Kubeflow Pipelines, ‘a platform for building and deploying portable, scalable machine learning (ML) workflows based on Docker containers.’
👍 6celia202004102021/07/18
シャッフルモード