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Question 85 - Professional Machine Learning Engineer discussion

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You are an ML engineer responsible for designing and implementing training pipelines for ML models. You need to create an end-to-end training pipeline for a TensorFlow model. The TensorFlow model will be trained on several terabytes of structured data. You need the pipeline to include data quality checks before training and model quality checks after training but prior to deployment. You want to minimize development time and the need for infrastructure maintenance. How should you build and orchestrate your training pipeline?

A.
Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Vertex AI Pipelines.
Answers
A.
Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Vertex AI Pipelines.
B.
Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Vertex AI Pipelines.
Answers
B.
Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Vertex AI Pipelines.
C.
Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
Answers
C.
Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
D.
Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
Answers
D.
Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
Suggested answer: B

Explanation:

The best option for creating and orchestrating an end-to-end training pipeline for a TensorFlow model is to use TensorFlow Extended (TFX) and standard TFX components, and deploy the pipeline to Vertex AI Pipelines. TFX is an end-to-end platform for deploying production ML pipelines, which consists of several built-in components that cover the entire ML lifecycle, from data ingestion and validation, to model training and evaluation, to model deployment and monitoring. TFX also supports custom components and integrations with other Google Cloud services, such as BigQuery, Dataflow, and Cloud Storage. Vertex AI Pipelines is a fully managed service that allows you to run TFX pipelines on Google Cloud, without having to worry about infrastructure provisioning, scaling, or maintenance. Vertex AI Pipelines also provides a user-friendly interface to monitor and manage your pipelines, as well as tools to track and compare experiments. The other options are not as suitable for creating and orchestrating an end-to-end training pipeline for a TensorFlow model, because:

Creating the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components would require more development time and effort, as Kubeflow Pipelines DSL is not as expressive or compatible with TensorFlow as TFX. Predefined Google Cloud components might not cover all the stages of the ML lifecycle, and might not be optimized for TensorFlow models.

Orchestrating the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine would require more infrastructure maintenance, as Kubeflow Pipelines is not a fully managed service, and you would have to provision and manage your own Kubernetes cluster. This would also incur more costs, as you would have to pay for the cluster resources, regardless of the pipeline usage.Reference:

TFX | ML Production Pipelines | TensorFlow

Vertex AI Pipelines | Google Cloud

Kubeflow Pipelines | Google Cloud

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asked 18/09/2024
AHOPlvaro Zorrilla
37 questions
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