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Question 103 - Professional Machine Learning Engineer discussion

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You are a lead ML engineer at a retail company. You want to track and manage ML metadata in a centralized way so that your team can have reproducible experiments by generating artifacts. Which management solution should you recommend to your team?

A.
Store your tf.logging data in BigQuery.
Answers
A.
Store your tf.logging data in BigQuery.
B.
Manage all relational entities in the Hive Metastore.
Answers
B.
Manage all relational entities in the Hive Metastore.
C.
Store all ML metadata in Google Cloud's operations suite.
Answers
C.
Store all ML metadata in Google Cloud's operations suite.
D.
Manage your ML workflows with Vertex ML Metadata.
Answers
D.
Manage your ML workflows with Vertex ML Metadata.
Suggested answer: D

Explanation:

Vertex ML Metadata is a service that lets you track and manage the metadata produced by your ML workflows in a centralized way. It helps you have reproducible experiments by generating artifacts that represent the data, parameters, and metrics used or produced by your ML system. You can also analyze the lineage and performance of your ML artifacts using Vertex ML Metadata.

Some of the benefits of using Vertex ML Metadata are:

It captures your ML system's metadata as a graph, where artifacts and executions are nodes, and events are edges that link them as inputs or outputs.

It allows you to create contexts to group sets of artifacts and executions together, such as experiments, runs, or projects.

It supports querying and filtering the metadata using the Vertex AI SDK for Python or REST commands.

It integrates with other Vertex AI services, such as Vertex AI Pipelines and Vertex AI Experiments, to automatically log metadata and artifacts.

The other options are not suitable for tracking and managing ML metadata in a centralized way.

Option A: Storing your tf.logging data in BigQuery is not enough to capture the full metadata of your ML system, such as the artifacts and their lineage. BigQuery is a data warehouse service that is mainly used for analytics and reporting, not for metadata management.

Option B: Managing all relational entities in the Hive Metastore is not a good solution for ML metadata, as it is designed for storing metadata of Hive tables and partitions, not for ML artifacts and executions. Hive Metastore is a component of the Apache Hive project, which is a data warehouse system for querying and analyzing large datasets stored in Hadoop.

Option C: Storing all ML metadata in Google Cloud's operations suite is not a feasible option, as it is a set of tools for monitoring, logging, tracing, and debugging your applications and infrastructure, not for ML metadata. Google Cloud's operations suite does not provide the features and integrations that Vertex ML Metadata offers for ML workflows.

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asked 18/09/2024
BETTE SLETTER
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