A data science team needs a centralized and organized location to store its various model versions, track their metadata, and easily deploy them to the respective applications. What Google Cloud…

The correct answer is B. Model Registry. A Model Registry (specifically part of Vertex AI Model Registry) is designed precisely for managing the lifecycle of machine learning models. It provides a centralized repository for storing, versioning, tracking metadata, and facilitating the deployment of models, which is…

Google Cloud MLOps Services

Question

A data science team needs a centralized and organized location to store its various model versions, track their metadata, and easily deploy them to the respective applications. What Google Cloud service should they use?

Options

ACloud Storage
BModel Registry
CBigQuery
DVertex AI Pipelines

How the community answered

(55 responses)

A
2% (1)
B
93% (51)
C
4% (2)
D
2% (1)

Explanation

A Model Registry (specifically part of Vertex AI Model Registry) is designed precisely for managing the lifecycle of machine learning models. It provides a centralized repository for storing, versioning, tracking metadata, and facilitating the deployment of models, which is essential for MLOps. Cloud Storage is for raw data, BigQuery for data warehousing, and Vertex AI Pipelines for workflow orchestration.

Topics

#MLOps#Model Management#Vertex AI#Model Deployment

Community Discussion

No community discussion yet for this question.

Full GENERATIVE-AI-LEADER Practice