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Question 247 - Professional Machine Learning Engineer discussion

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While running a model training pipeline on Vertex Al, you discover that the evaluation step is failing because of an out-of-memory error. You are currently using TensorFlow Model Analysis (TFMA) with a standard Evaluator TensorFlow Extended (TFX) pipeline component for the evaluation step. You want to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead. What should you do?

A.
Add tfma.MetricsSpec () to limit the number of metrics in the evaluation step.
Answers
A.
Add tfma.MetricsSpec () to limit the number of metrics in the evaluation step.
B.
Migrate your pipeline to Kubeflow hosted on Google Kubernetes Engine, and specify the appropriate node parameters for the evaluation step.
Answers
B.
Migrate your pipeline to Kubeflow hosted on Google Kubernetes Engine, and specify the appropriate node parameters for the evaluation step.
C.
Include the flag -runner=DataflowRunner in beam_pipeline_args to run the evaluation step on Dataflow.
Answers
C.
Include the flag -runner=DataflowRunner in beam_pipeline_args to run the evaluation step on Dataflow.
D.
Move the evaluation step out of your pipeline and run it on custom Compute Engine VMs with sufficient memory.
Answers
D.
Move the evaluation step out of your pipeline and run it on custom Compute Engine VMs with sufficient memory.
Suggested answer: C

Explanation:

The best option to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead is to use Dataflow as the runner for the evaluation step. Dataflow is a fully managed service for executing Apache Beam pipelines that can scale up and down according to the workload. Dataflow can handle large-scale, distributed data processing tasks such as model evaluation, and it can also integrate with Vertex AI Pipelines and TensorFlow Extended (TFX). By using the flag-runner=DataflowRunnerinbeam_pipeline_args, you can instruct the Evaluator component to run the evaluation step on Dataflow, instead of using the default DirectRunner, which runs locally and may cause out-of-memory errors. Option A is incorrect because addingtfma.MetricsSpec()to limit the number of metrics in the evaluation step may downgrade the evaluation quality, as some important metrics may be omitted. Moreover, reducing the number of metrics may not solve the out-of-memory error, as the evaluation step may still consume a lot of memory depending on the size and complexity of the data and the model. Option B is incorrect because migrating the pipeline to Kubeflow hosted on Google Kubernetes Engine (GKE) may increase the infrastructure overhead, as you need to provision, manage, and monitor the GKE cluster yourself. Moreover, you need to specify the appropriate node parameters for the evaluation step, which may require trial and error to find the optimal configuration. Option D is incorrect because moving the evaluation step out of the pipeline and running it on custom Compute Engine VMs may also increase the infrastructure overhead, as you need to create, configure, and delete the VMs yourself. Moreover, you need to ensure that the VMs have sufficient memory for the evaluation step, which may require trial and error to find the optimal machine type.Reference:

Dataflow documentation

Using DataflowRunner

Evaluator component documentation

Configuring the Evaluator component

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asked 18/09/2024
monet washington
35 questions
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