Data science collaboration hub
The most lightweight experiment management tool that fits any workflow.
Use as a service or deploy on any cloud or your own hardware.
#1. HOW IT WORKS
Neptune allows you to track the entire experimentation process
Everything is backed-up and organized, ready to be accessed, reproduced and shared with others.
Track and version your notebooks
All you need is to install a Jupyter extension.
Manage your experimentation process
Neptune tracks your work with virtually no interference to the way you like to do it. Decide what is relevant to your project and start tracking:
Integrate with your workflow easily
Neptune is a lightweight extension to your current workflow. Works with all common technologies in data science domain and integrates with other tools. It will take you 5 minutes to get started.
# Track your notebooks
pip install neptune-notebooks
jupyter nbextension enable ——py neptune-notebooks
# Track all experiment-related objects
# Host MLflow or TensorBoard runs on Neptune
neptune@ubuntu:~$ neptune tensorboard path/to/logdir
neptune@ubuntu:~$ neptune mlflow path/to/project
Compare notebooks like source code
Record your exploration process and analyze diffs between checkpoints. Select two notebooks and compare their content, code and outputs, side-by-side just like source code.
Version and compare experiments
Keep track of your progress and reproduce results easily. Neptune tracks all the details about every single experiment you run. You can tag, filter, sort and compare your experiments.
Perfect for teams, loved by individuals
Reply.ai team leverages remote work while keeping results in a common environment.
At Reply.ai our goal is to make customer services faster and smarter
by automating repetitive processes and delivering instant and
personalized attention on messaging channels.
We're developing products that leverage Natural Language Processing to improve customer care. And as a fully remote team, we were looking for a tool that would allow us to track experiments, compare results, and share both datasets and models, without changing our usual workflow.
As a team leader I'm also very concerned about the reproducibility of our experiments, especially when different data scientists and Machine Learning engineers have their own ways of doing things. It's not always easy to follow software engineering best practices.
For me the most important thing about Neptune is its flexibility. Even if I'm training with Keras or Tensorflow in my local laptop, while my team folks are using fast.ai on a virtual machine, we can share our results in a common environment. Also, thanks to Neptune's Query API, our backend team, which is in charge of models deployment, is able to programmatically access all the experiments we run and fetch the best model.
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.
Here at Deepsense.ai we are providing machine and deep learning solutions and consultancy for market leaders such as BCG, IBM, Juniper, EY, nielsen, nVidia, and Loreal. Using Neptune, we are able 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 are really necessary to make strategic decisions, since clients have real time access to what we do and we can now collaborate on a regular basis. When everything is tracked and organized in the one knowledge centre we don’t need to create much additional documentation for our clients. With Neptune we are able to deliver our projects faster and we have optimized time spent onboarding new data scientists to a project.
An Individual account allows for collaboration of multiple projects in a single view.
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 do collaborative work.
Plans that suit every data scientist
up to 3 collaborators per private projects
Would you like to deploy Neptune on your cloud or own infrastructure?
#4. EXAMPLE pROJECTS
User Data at Rest
User Data in Transit
We encrypt any traffic that travels over an open network, including all data transfers.