ML Metadata Store: What It Is and Why It Matters?

Most people who find this article want to improve their model-building process.

Others have already deployed the first models to production, but they don’t know how those models were created or which data was used.

Some people already have many models in production, but orchestrating model A/B testing, switching challengers and champions, or triggering, testing, and monitoring re-training pipelines is not great.

If you see yourself in one of those groups, or somewhere in between, I can tell you that ML metadata store can help with all of those things and then some others as well.

You may need to connect it to other MLOps tools or your CI/CD pipelines, but it will simplify managing models in most workflows.

…but so do experiment tracking, model registry, model store, model catalog, and other model-related animals.

So what is an ML metadata store exactly, how is it different from those other model things, and how can it help you build and deploy models with more confidence?

This is what this article is about.