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The most lightweight experiment tracking tool for machine learning
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What is Neptune?

It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools.
Neptune is a tool in the Machine Learning Tools category of a tech stack.

Who uses Neptune?


12 developers on StackShare have stated that they use Neptune.

Neptune Integrations

R Language, PyTorch, Keras, Matplotlib, and MLflow are some of the popular tools that integrate with Neptune. Here's a list of all 6 tools that integrate with Neptune.
Pros of Neptune
Aws managed services
Supports both gremlin and openCypher query languages

Neptune's Features

  • Experiment tracking
  • Experiment versioning
  • Experiment comparison
  • Experiment monitoring
  • Experiment sharing
  • Notebook versioning

Neptune Alternatives & Comparisons

What are some alternatives to Neptune?
Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.
Dgraph's goal is to provide Google production level scale and throughput, with low enough latency to be serving real time user queries, over terabytes of structured data. Dgraph supports GraphQL-like query syntax, and responds in JSON and Protocol Buffers over GRPC and HTTP.
It is a web development framework written in F# which implements the server-side MVC pattern. Many of its components and concepts will seem familiar to anyone with experience in other web frameworks like Ruby on Rails or Python’s Django.
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
See all alternatives

Neptune's Followers
35 developers follow Neptune to keep up with related blogs and decisions.