The model registry component of the MLOps stack serves as a good connection between Data Scientists and ML Engineers.
DS can register production-ready models, and MLEs can access them and push them to production.
All the information related to the model is there.
Model weights, parameters, info about the dataset it was trained on, or about the person that registered it – basically, any metadata that might be needed.
Having this one source of truth for all the models makes the work much more organized and saves a lot of time. Plus, it gives you peace of mind when someone says āauditā.
In this video, Parth Tiwary presents Neptuneās model registry capabilities.
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Register a model
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Create model version
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Associate metadata with model version
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Resume a model version
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Viewing models and model versions in the web app
Learn more
Check Neptune’s model registry documentation to learn how to use it.
More about How to Store and Manage ML Models Using Model Registry
See also
.upload(“product_updates_september_2023”)
See also
What is a Project in Neptune?
See also
Model Training: Detectron2 + neptune.ai Integration [Example]
See also
Model Training: Prophet + neptune.ai Integration [Example]
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