We Raised $8M Series A to Continue Building Experiment Tracking and Model Registry That “Just Works”

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Have all your production-ready models
in a centralized model registry

Version production-ready models and metadata associated with them in a single place.
Review models and transition them between development stages.
Access all models your team created via API or browse them in the UI.

Register a model

Register a production-ready model.

You can attach any metadata or artifacts to it and organize them in any structure you want.

model = neptune.init_model(
    name="face_detection", key="DET",
)
model["validation/dataset"].track_files(
    "s3://datasets/validation")
See docs

Create model version

For any registered model, create as many model versions as you want.

Again, you can attach whatever metadata you want to it.

model_version = neptune.init_model_version(
    model="FACE-DET",
)
model_version["model/binary"].upload(
    "model.pt")
model_version["validation/acc"] = 0.97
See docs

Version external model artifacts

Save hash, location and other model artifact metadata.

You don’t have to upload the model to Neptune.

Just keep track of the model reference to local or S3-compatible storage.

model_version["model/binary"].track_files(
    "model.pt")
See docs

Review and change stages

Look at the validation, test metrics and other model metadata and approve stage transitions.

You can move models between None/Staging/Production/Archived.

model_version.change_stage("staging")
See docs

Access and share models

Every model and model version is accessible via Neptune App or through the API.

Once you have all the model artifacts you can deploy your model in your production pipelines or serve it via API.

import neptune.new as neptune

model_version = neptune.init_model_version(
    version="FACE-DET-42")
model_version["model/signature"].download()
See docs
Core Concepts

Resources

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Manage all your model metadata in a single place