Exam Professional Machine Learning Engineer topic 1 question 8 discussion

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.

the 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

Req: retrain the model every month+ Google-recommended best practice+ end-to-end architecture A. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model. : Supports all above B. Use a model trained and deployed on BigQuery ML, and trigger retraining with the scheduled query feature in BigQuery : Why BigQuery ML when vertexAI/kubflow can handle end to end. BigQuery ML+ traigger only initiate the code run. C. Write a Cloud Functions script that launches a training and deploying job on AI Platform that is triggered by Cloud Scheduler. : Not recommended by google for end to end ML D. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model. : Not recommended by google for end to end ML. what if model fails? matrix monitor?

A) Kubeflow Pipelines is the answer.

Chose A

D - Dataflow job can't deploy models B,C are not - are not complete solutions leaving A to be the correct one

Answer is A

Went with A

A : Yet the newer is Vertext-AI Pipeline built on Kubeflow

A : In this case, it would be a good fit as you need to retrain your model every month, which can be automated with Kubeflow Pipelines. This makes it easier to manage the entire process, from training to deploying, in a streamlined and scalable manner.

A is correct All the options get you to the required result, but only A follows the Google-recommended best practices

Answer is A: Kubeflow Pipelines can form an end-to-end architecture

Correct answer is "A"

Community vote

A for me too. KF provides all the end2end tools to perform what is asked

A Kubeflow can handle all of those things, including deploying to a model endpoint for real-time serving.

ANS: A https://medium.com/google-cloud/how-to-build-an-end-to-end-propensity-to-purchase-solution-using-bigquery-ml-and-kubeflow-pipelines-cd4161f734d9#75c7 To 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.’