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

Neptune in 30 seconds

Step 1: Connect Neptune to your code

import neptune.new as neptune
run = neptune.init(project="corp_space/fraud_detection")

Step 2: 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 3: See it live in the UI

Getting started

• ​Sign up and explore yourself​

• ​Run interactive tutorial (no signup needed)​

• ​Hello World​

• ​Core concepts​

Integrations with the ML ecosystem

• ​PyTorch Lightning​

• ​PyTorch​

• ​Tensorflow / Keras​

• ​Optuna​

• ​Scikit Learn​

• ​See full list ​

Experiment Tracking

• ​Logging metadata​

• ​What you can log and display​

• ​Organize ML experiments​

• ​Monitor model training runs live​

Model Registry

• ​Log model building metadata​

• ​Querying and downloading metadata​

Need help, questions?

• ​Getting help​