patrycjaneptune

Do you want to keep track DATASET VERSIONS for every model training run to reproduce your results and models?

You can now do it with Neptune:

Query the dataset version from previous runs to make sure you are training on the same dataset version

Group your Neptune Runs by the dataset version they were trained on

See if models were trained on the same dataset version

Compare datasets in the Neptune UI to see what changed

Explore artifacts in the docs.

A few months ago, some of our users asked if we could connect the experiment and metadata management capabilities of neptune.ai with all the great stuff that Kedro does.

Finally, we can say, we did the Kedro-Neptune plugin is out!

What do you get with it?

All the benefits of a nicely organized kedro pipeline with a powerful Neptune UI for filtering, comparing, displaying, and organizing ML metadata generated in pipelines and nodes

Explore the documentation, examples, use-cases, and video tutorial in the link provided above. And let me know what you think.

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.