You are building a MLOps platform to automate your company’s ML experiments and model retraining. You need to organize the artifacts for dozens of pipelines. How should you store the pipelines’ artifacts?
A.
Store parameters in Cloud SQL, and store the models’ source code and binaries in GitHub.
B.
Store parameters in Cloud SQL, store the models’ source code in GitHub, and store the models’ binaries in Cloud Storage.
C.
Store parameters in Vertex ML Metadata, store the models’ source code in GitHub, and store the models’ binaries in Cloud Storage.
D.
Store parameters in Vertex ML Metadata and store the models’ source code and binaries in GitHub.
Vertex ML Metadata: This service is specifically designed to store and track metadata for ML pipelines, including parameters. It provides a centralized location to manage and query pipeline execution details, making it ideal for dozens of pipelines.
Cloud Storage: This is a scalable and cost-effective storage solution for model binaries. It integrates well with Vertex AI and other cloud services.
GitHub: While not a Google Cloud service, it's a popular version control system well-suited for storing and managing your models' source code, particularly for collaboration among team members.
A. Cloud SQL for Parameters: While Cloud SQL is a relational database service, Vertex ML Metadata offers a dedicated solution for ML metadata management, including parameters, providing better integration and functionality within the MLOps context.
D. Vertex ML Metadata for Source Code and Binaries: Vertex ML Metadata is primarily focused on ML pipeline metadata and experiment tracking. Cloud Storage is a more appropriate service for storing large binary files like model artifacts.
A. Cloud SQL and GitHub: Cloud SQL isn't designed for ML metadata management, potentially leading to challenges in tracking experiment details and lineage.
B. Cloud SQL, GitHub, and Cloud Storage: While viable, this approach misses the benefits of Vertex ML Metadata for organized ML artifact management.
D. Vertex ML Metadata and GitHub: Storing model binaries in GitHub can be inefficient for large files and might incur higher storage costs.
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