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

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ML Metadata Store

A single place to manage all your model building metadata.
Track experiments. Register models.
Integrate with everything else.

Overview

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Experiment tracking

Track, organize, and compare everything you care about in your ML experiments.

  • Monitor experiments as they are running
  • Keep track of metrics, parameters, diagnostic charts, and more 
  • Search, group, and compare experiments with no effort
  • Drill down to every experiment information you need
  • Share results with your team and access all experiment details programmatically
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Be more productive

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.
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Integrations

Get started in 5 minutes

1
Sign up to Neptune ->
and install client library
pip install neptune-client
2
Track experiments
import neptune.new as neptune

run = neptune.init('Me/MyProject')
run['params'] = {'lr':0.1, 
    'dropout':0.4}
run['test_accuracy'] = 0.84
3
Register models
model_version = neptune.init
    _model_version(model="FACE-DET")
model_version["model/binary"]
    .track_files("model.pt")
model_version["validation/acc"]
     = 0.97

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Resources

Code examples, videos, projects gallery, and other resources.

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Manage all your model metadata in a single place