Professional Data EngineerOperationalizing machine learning modelsMedium
A data engineering team is responsible for managing several machine learning models in production. They need a centralized repository for storing model artifacts, versioning models, and tracking metadata such as training parameters, evaluation metrics, and the datasets used for training. This repository should facilitate collaboration among data scientists and ensure reproducibility. Which Google Cloud service is designed for this purpose?
- ACloud Storage
- BContainer Registry
- CCloud Source Repositories
- DVertex AI Model Registry
Show answer & explanationAnswer & explanation
Correct answer: D. Vertex AI Model Registry
Vertex AI Model Registry is specifically designed to centralize the management of ML models, providing functionalities for versioning, storing metadata, tracking lineage, and facilitating model governance and collaboration, which goes beyond simple artifact storage.
Why the other options are wrong
- A. Cloud Storage is for storing raw files and objects, not for managing ML model versions and associated metadata in a structured way.
- B. Container Registry stores Docker images, which can contain models, but it lacks ML-specific metadata tracking and versioning features for the model itself.
- C. Cloud Source Repositories is for source code management, not for compiled model artifacts and their associated ML metadata.
Vertex AI Model Registry
A centralized repository within Vertex AI for managing machine learning models, including their versions, metadata, lineage, and deployment configurations, to enable governance and reproducibility.
- Stores model artifacts and associated metadata.
- Supports model versioning and lineage tracking.
- Facilitates model governance and collaboration.
Memory trick: The Model Registry is your library for all ML models, versions, and their stories.