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.create_experiment(params={'lr':0.1, 'dropout':0.4})
# training and evaluation logic
neptune.log_metric('test_accuracy', 0.84)
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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

“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 on a virtual machine, we can share our results in a common environment.”

Víctor Peinado
Senior NLP/ML Engineer

“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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