Data science work sharing hub.
No credit card required. Free for individuals and teams to use.
Takes 5 minutes to get started.
#1. PRODUCT MANIFESTO
Fits into your workflow,
not the other way around.
User friendly everywhere,
from API to UI.
Keeps your work safeguarded,
no matter what.
#2. HOW IT WORKS
Neptune tracks your work with virtually no interference to the way you like to do it. You focus on ideas and experiments, Neptune will take care of the rest.
- Data versions
- Model files
- Source code
# Track your model training from Python with Neptuneimport neptune
neptune.send_metric(’auc’, score) neptune.send_image(’roc_auc_curve’, PIL_image) neptune.send_artifact(’my_model.h5’)
# Host runs and notebooks at Neptuneneptune@ubuntu:~$ neptune tensorboard path/to/logdir neptune@ubuntu:~$ neptune mlflow path/to/project
Neptune organizes your data science projects automatically, transforming them into a knowledge repository. Every byte of knowledge is indexed and searchable.
- Experiment comparison
- Project docs
- User management
- Custom views
- Queryable API
- Shareable links
- Project contributor invites
Perfect for teams, loved by individuals
NewYorker is benefiting from keeping track of machine learning experiments.
New Yorker is a leading German clothing retailer managing over 1000 branches spread across 40 countries. Our data science team focuses on price forecasting and object detection. Neptune allows us to keep everything we want to know about experiments in one centralized place where our team can easily access it. What we really like about Neptune is that it easily hooks into multiple frameworks. Keeping track of machine learning experiments systematically over time and visualising clearly the output adds a lot of value for us.
Symmetrical is speeding up the machine learning model training process thank to the process organization.
At Symmetrical Labs we develop a marketplace for financial products by connecting consumers to the right products in real-time. We discovered neptune.ml to be an invaluable tool for running machine learning experiments in an extremely fast manner. Even more importantly, we learned that by using it we can significantly shorten the amount of time we spend on setting up and running experiments. We have incorporated Neptune in our pipeline for developing, testing and fine-tuning algorithms for selecting the financial products tailored to our clients, which is a big win for us.
Collaboration is enabling deepsense.ai to build top quality machine learning models for their clients.
At deepsense.ai we provide machine learning and deep learning solutions and consultancy for market leaders such as BCG, IBM, Juniper, EY, Nielsen, NVIDIA, L'Oréal. Using Neptune enables us to cooperate more closely with our customers and eliminate most of the problems related to project communication. We limited the number of meetings to the ones that were really necessary to make strategic decisions since clients have real-time access to what we do and we collaborate on a regular basis. When everything is tracked and organized in the one knowledge center we don’t need to create much additional documentation for our clients. We also realized that with Neptune we were able to deliver our projects faster and we optimized the time spent onboarding, which we found very beneficial.
Lucas is using Individual account to organize his data science projects.
As a graduate student and research enthusiast, I'm working with both classical machine learning problems like classification or regression and deep learning, especially with recurrent neural networks. I was trying to handle a bunch of different projects, they were all scattered among different platforms like Azure or Google Colab. Each of those platforms has a feature that I like - but Neptune puts it all together with everything in one place which makes my projects way easier to manage. The Collaboration part of Neptune is also really important for me. One of the things that I'm always looking for when choosing a machine learning framework or environment is the ability to share my work and to do collaborative work.
up to 3 collaborators per private project
Would you like to deploy Neptune on your cloud or own infrastructure?
#5. DATA SECURITY
User Data at Rest
User Data in Transit
We encrypt any traffic that travels over an open network, including all data transfers.