Day 12 - GCP ML

The major benefit of the Lineage tracking feature of AI Platform pipelines is:

• To track artifacts for an ML pipeline

• To provide history and versions of your ML model

• To share of ML pipeline within your organization

• None of the above

The simplest way to launch a training task on AI platform from a Kubeflow task is

• Launch the training job from the Kubeflow UI

• Use the AI platform training pre-built Kubeflow component

• To package your AI platform training script into a Docker container

• Launch the training job from the Kubeflow SDK

You also want to use AI Platform's continuous evaluation service to ensure that the models have high accuracy on your test dataset. What should you do?

• Keep the original test dataset unchanged even if newer products are incorporated into retraining.

• Extend your test dataset with images of the newer products when they are introduced to retraining.

• Replace your test dataset with images of the newer products when they are introduced to retraining.

• Update your test dataset with images of the newer products when your evaluation metrics drop below a pre-decided threshold.

What is the cause of steady decline in model accuracy

• Poor data quality

• lack of model retraining

• incorrect split data for training and testing

• too few layers in the model

Which of the following is a managed service on GCP

• AI Platform Pipelines

• Kubeflow on AI Platform

• Vertex AI Pipelines

• TFX on AI Platform