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Neptune is a metadata store for MLOps, built for teams that run a lot of experiments.‌
It gives you a single place to log, store, display, organize, compare, and query all your model-building metadata.
‌Neptune is used for:‌
  • Experiment tracking: Log, display, organize, and compare ML experiments in a single place.
  • Model registry: Version, store, manage, and query trained models and model building metadata.
  • Monitoring ML runs live: Record and monitor model training, evaluation, or production runs live.
Explainer Video

Neptune in 30 seconds

Step 1: Install the Neptune client
Depending on your operating system, open a Terminal or Command Prompt window and enter the following command:
pip
conda
pip install neptune-client
conda install -c conda-forge neptune-client
For more help, see Installation and setup.
Step 2: Connect Neptune to your code
import neptune.new as neptune
run = neptune.init(project="corp_space/fraud_detection")
Step 3: Log metadata
run["parameters"] = {"lr": 0.001, "optim": "Adam"} # parameters
run["f1_score"] = 0.66 # metrics
run["roc_curve"].upload("roc_curve.png") # charts
run["model"].upload("model.h5") # models
Step 4: See it live in the app
Model building metadata in the Neptune app

Getting started

If you're planning on using Neptune with R instead of Python, head to our dedicated section for R.

Integrations with the ML ecosystem

Experiment tracking

Model registry

Need help, have questions?

Last modified 3mo ago