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…

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.