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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
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pip install neptune-client
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conda install -c conda-forge neptune-client
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Step 2: Connect Neptune to your code
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import neptune.new as neptune
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run = neptune.init(project="corp_space/fraud_detection")
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Step 3: Log metadata
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run["parameters"] = {"lr": 0.001, "optim": "Adam"} # parameters
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run["f1_score"] = 0.66 # metrics
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run["roc_curve"].upload("roc_curve.png") # charts
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run["model"].upload("model.h5") # models
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Step 4: See it live in the app
Model building metadata in the Neptune app
Getting started
Integrations with the ML ecosystem
Experiment tracking
Model registry
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Last modified 2mo ago