ML Metadata Store
A single place to manage all your
A single place to manage all your
model-building metadata
Track experiments, register models, and integrate with any MLOps tool stack



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
Get started in 5 minutes
1
Sign up to Neptune and install client library
pip install neptune
2
Track experiments
import neptune
run = neptune.init_run()
run["params"] = {
"lr": 0.1, "dropout": 0.4
}
run["test_accuracy"] = 0.84
3
Register models
import neptune
model = neptune.init_model()
model["model"] = {
"size_limit": 50.0,
"size_units": "MB",
}
model["model/signature"].upload(
"model_signature.json")