We Raised $8M Series A to Continue Building Experiment Tracking and Model Registry That “Just Works”
ML Metadata Store
A single place to manage all your model building metadata.
Track experiments. Register models.
Integrate with everything else.
Overview
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
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.
Integrations
Get started in 5 minutes
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
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“I’ve been mostly using Neptune just looking at the UI which I have, let’s say, kind of tailored to my needs. So I added some custom columns which will enable me to easily see the interesting parameters and based on this I’m just shifting over the runs and trying to capture what exactly interests me.”
“Gone are the days of writing stuff down on google docs and trying to remember which run was executed with which parameters and for what reasons. Having everything in Neptune allows us to focus on the results and better algorithms.“
“Neptune is aesthetic. Therefore we could simply use the visualization it was generating in our reports.
We trained more than 120.000 models in total, for more than 7000 subproblems identified by various combinations of features. Due to Neptune, we were able to filter experiments for given subproblems and compare them to find the best one. Also, we stored a lot of metadata, visualizations of hyperparameters’ tuning, predictions, pickled models, etc. In short, we were saving everything we needed in Neptune.”
“The way we work is that we do not experiment constantly. After checking out both Neptune and Weights and Biases, Neptune made sense to us due to its pay-per-use or usage-based pricing. Now when we are doing active experiments then we can scale up and when we’re busy integrating all our models for a few months that we scale down again.”