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Question 147 - Professional Machine Learning Engineer discussion

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You developed a Vertex Al ML pipeline that consists of preprocessing and training steps and each set of steps runs on a separate custom Docker image Your organization uses GitHub and GitHub Actions as CI/CD to run unit and integration tests You need to automate the model retraining workflow so that it can be initiated both manually and when a new version of the code is merged in the main branch You want to minimize the steps required to build the workflow while also allowing for maximum flexibility How should you configure the CI/CD workflow?

A.
Trigger a Cloud Build workflow to run tests build custom Docker images, push the images to Artifact Registry and launch the pipeline in Vertex Al Pipelines.
Answers
A.
Trigger a Cloud Build workflow to run tests build custom Docker images, push the images to Artifact Registry and launch the pipeline in Vertex Al Pipelines.
B.
Trigger GitHub Actions to run the tests launch a job on Cloud Run to build custom Docker images push the images to Artifact Registry and launch the pipeline in Vertex Al Pipelines.
Answers
B.
Trigger GitHub Actions to run the tests launch a job on Cloud Run to build custom Docker images push the images to Artifact Registry and launch the pipeline in Vertex Al Pipelines.
C.
Trigger GitHub Actions to run the tests build custom Docker images push the images to Artifact Registry, and launch the pipeline in Vertex Al Pipelines.
Answers
C.
Trigger GitHub Actions to run the tests build custom Docker images push the images to Artifact Registry, and launch the pipeline in Vertex Al Pipelines.
D.
Trigger GitHub Actions to run the tests launch a Cloud Build workflow to build custom Dicker images, push the images to Artifact Registry, and launch the pipeline in Vertex Al Pipelines.
Answers
D.
Trigger GitHub Actions to run the tests launch a Cloud Build workflow to build custom Dicker images, push the images to Artifact Registry, and launch the pipeline in Vertex Al Pipelines.
Suggested answer: D

Explanation:

The best option for automating the model retraining workflow is to use GitHub Actions and Cloud Build. GitHub Actions is a service that can create and run workflows for continuous integration and continuous delivery (CI/CD) on GitHub. GitHub Actions can run tests, build and deploy code, and trigger other actions based on events such as code changes, pull requests, or manual triggers. Cloud Build is a service that can create and run scalable and reliable pipelines to build, test, and deploy software on Google Cloud. Cloud Build can build custom Docker images, push the images to Artifact Registry, and launch the pipeline in Vertex AI Pipelines. Vertex AI Pipelines is a service that can orchestrate machine learning (ML) workflows using Vertex AI. Vertex AI Pipelines can run preprocessing and training steps on custom Docker images, and evaluate, deploy, and monitor the ML model. By using GitHub Actions and Cloud Build, users can leverage the power and flexibility of Google Cloud to automate the model retraining workflow, while minimizing the steps required to build the workflow.

The other options are not as good as option D, for the following reasons:

Option A: Triggering a Cloud Build workflow to run tests, build custom Docker images, push the images to Artifact Registry, and launch the pipeline in Vertex AI Pipelines would require more configuration and maintenance than using GitHub Actions and Cloud Build. Cloud Build is a service that can create and run pipelines to build, test, and deploy software on Google Cloud, but it is not designed to integrate with GitHub or other source code repositories.To trigger a Cloud Build workflow from GitHub, users would need to set up a webhook, a Cloud Pub/Sub topic, and a Cloud Function1.Moreover, Cloud Build does not support manual triggers, which limits the flexibility of the workflow2.

Option B: Triggering GitHub Actions to run the tests, launching a job on Cloud Run to build custom Docker images, pushing the images to Artifact Registry, and launching the pipeline in Vertex AI Pipelines would require more steps and resources than using GitHub Actions and Cloud Build. Cloud Run is a service that can run stateless containers on a fully managed environment or on Anthos. Cloud Run can build custom Docker images, but it is not optimized for this task.Users would need to write a Dockerfile, a cloudbuild.yaml file, and a Cloud Run service configuration file, and use the gcloud command-line tool to build and deploy the image3. Moreover, Cloud Run is designed for serving HTTP requests, not for running ML pipelines, which can have different performance and scalability requirements.

Option C: Triggering GitHub Actions to run the tests, building custom Docker images, pushing the images to Artifact Registry, and launching the pipeline in Vertex AI Pipelines would require more skills and tools than using GitHub Actions and Cloud Build. GitHub Actions can run tests and build code, but it is not specialized for building Docker images. Users would need to install and configure Docker on the GitHub Actions runner, write a Dockerfile, and use the docker command-line tool to build and push the image. Moreover, GitHub Actions has limitations on the disk space, memory, and CPU of the runner, which can affect the speed and reliability of the image building process.

Building CI/CD for Vertex AI pipelines: The first solution

Cloud Build

GitHub Actions

Vertex AI Pipelines

Triggering builds from GitHub

Triggering builds manually

Building containers

Cloud Run

[Building and testing Docker images with GitHub Actions]

[Usage limits, billing, and administration]

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asked 18/09/2024
Saphronia Yancey
36 questions
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