Manage all your model building metadata in a single place

Log, store, display, organize, compare and query all your MLOps metadata.
Experiment tracking and model registry built for research and production teams that run a lot of experiments.

10.000 + ML engineers and researchers manage their experiment and model metadata in Neptune

Feel in control of your model building and experimentation

  • Record everything you care about for every ML job you run
  • Know on which dataset, parameters, and code every model was trained on
  • Have all the metrics, charts, and any other ML metadata organized in a single place
  • Make your model training runs reproducible and comparable with almost no extra effort
  • Have everything backed up and accessible from anywhere
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Be more productive at ML engineering and research

  • Don’t waste time looking for folders and spreadsheets with models or configs. Have everything easily accessible in one place
  • Cut unproductive meetings by sharing results, dashboards, or logs with a link
  • Reduce context switching by having everything you need in a single dashboard
  • Find the information you need quickly in a dashboard that was built for ML model management
  • Debug and compare your models and experiments with no extra effort
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Be more productive

Focus on ML, leave metadata bookkeeping to us

  • We set up and maintain metadata databases and dashboards for you (we deploy on-prem too)
  • We implement and update loggers for all major ML libraries 
  • We optimize loggers/databases/dashboards to work for millions of experiments and models
  • We help your team get started with excellent examples, documentation, and a support team ready to help at any time
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Focus on ML

Use computational resources more efficiently

  • See what your team is working on and stop duplicating expensive training runs
  • Know when your runs fail and react right away
  • Don’t re-run experiments because you forgot to track parameters. Make experiments reproducible and run them once
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Hardware usage

Build reproducible, compliant, and traceable models

  • Make every ML job reproducible. Keep track of everything you need
  • Have your models and experiments backed up and accessible even years after
  • Know who trained a model, on what dataset, code, and parameters. Do it for every model you build
  • Be compliant by keeping a record of everything that happens in your model development
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Model metadata

Need more info about Neptune?

Get started in 5 minutes

1. Create a free account
Sign up
2. Install Neptune client library
pip install neptune-client
3. Add logging to your script
import neptune

neptune.init('Me/MyProject')
neptune.create_experiment(params={'lr':0.1, 'dropout':0.4})
# training and evaluation logic
neptune.log_metric('test_accuracy', 0.84)
Try live notebook

Use one of Neptune’s 25+ integrations

With Neptune you can get more out of tools you use
every day. Neptune comes with 25+ integrations with
libraries used in machine learning, deep learning and
reinforcement learning.

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See what our users are saying

“Neptune allows us to keep all of our experiments organized in a single space. Being able to see my team’s work results any time I need makes it effortless to track progress and enables easier coordination.”

Michael Ulin
VP, Machine Learning @Zesty.ai

“For me the most important thing about Neptune is its flexibility. Even if I’m training with Keras or Tensorflow on my local laptop, and my colleagues are using fast.ai on a virtual machine, we can share our results in a common environment.”

Víctor Peinado
Senior NLP/ML Engineer @reply.ai

“Within the first few tens of runs, I realized how complete the tracking was – not just one or two numbers, but also the exact state of the code, the best-quality model snapshot stored to the cloud, the ability to quickly add notes on a particular experiment. My old methods were such a mess by comparison.”

Edward Dixon
Enterprise Data Scientist @Intel

“Neptune is making it easy to share results with my teammates. I’m sending them a link and telling what to look at, or I’m building a View on the experiments dashboard. I don’t need to generate it by myself, and everyone in my team have access to it.”

Ronert Obst
Head of Data Science @New Yorker

“Neptune is making it easy to share results with my teammates. I’m sending them a link and telling what to look at, or I’m building a View on the experiments dashboard. I don’t need to generate it by myself, and everyone in my team have access to it.”

Maciej Bartczak
Resarch Lead @Banacha Street

“Without information, I have in the Monitoring section I wouldn’t know that my experiments are running 10 times slower than they could. All of my experiments are being trained on separate machines which I can access only via ssh. If I would need to download and check all of this separately I would be rather discouraged. When I want to share my results I’m simply sending a link.”

Michał Kardas
Machine Learning Researcher @TensorCell

“Previously used tensorboard and azureml but Neptune is hugely better. In particular, getting started is really easy; documentation is excellent, and the layout of charts and parameters is much clearer.”

Simon Mackenzie
AI Engineer and Data Scientist
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Focus on building models.
Leave metadata bookkeeping to Neptune.
Get started in 5 minutes.

Not convinced?

Try in a live Notebook (zero setup, no registration)

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Explore example project

Example project
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