Managing Your ML Assets

In a production ML system, you will have many different versions of your datasets, models, and other ML assets. It's crucial to have a system for managing these assets to ensure reproducibility, facilitate collaboration, and simplify deployment.

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1. Vertex AI Model Registry

The Vertex AI Model Registry is a central repository for managing your ML models. It allows you to:

• Version your models: The Model Registry automatically assigns a version number to each new model that you upload.
• Alias your models: You can create aliases (e.g., "production", "staging") to point to specific versions of your models. This makes it easy to deploy different versions of your models to different environments.
• Track model metadata: The Model Registry automatically tracks the metadata for your models, such as the hyperparameters, evaluation metrics, and the dataset it was trained on.

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2. Versioning Datasets

There are several ways to version your datasets:

• Google Cloud Storage: You can use versioning in Google Cloud Storage to keep a history of your dataset files.
• BigQuery: You can use table snapshots in BigQuery to create a point-in-time copy of your dataset.
• Vertex AI Datasets: You can use Vertex AI Datasets to manage different versions of your datasets.

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3. Versioning Code

You should use a version control system like Git to manage your ML code. This will allow you to track changes to your code, collaborate with other developers, and easily roll back to previous versions if necessary.

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