Alternatives to Neptune logo

Alternatives to Neptune

Neo4j, Dgraph, Saturn, TensorFlow, and PyTorch are the most popular alternatives and competitors to Neptune.
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What is Neptune and what are its top alternatives?

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.

Top Alternatives to Neptune

  • Neo4j

    Neo4j

    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

    Dgraph

    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. ...

  • Saturn

    Saturn

    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

    TensorFlow

    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

    PyTorch

    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. ...

  • Keras

    Keras

    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/ ...

  • scikit-learn

    scikit-learn

    scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license. ...

  • CUDA

    CUDA

    A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation. ...

Neptune alternatives & related posts

Neo4j logo

Neo4j

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1.1K
340
The world’s leading Graph Database
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1.1K
+ 1
340
PROS OF NEO4J
  • 68
    Cypher – graph query language
  • 58
    Great graphdb
  • 31
    Open source
  • 29
    Rest api
  • 27
    High-Performance Native API
  • 24
    ACID
  • 20
    Easy setup
  • 15
    Great support
  • 10
    Clustering
  • 9
    Hot Backups
  • 8
    Great Web Admin UI
  • 7
    Powerful, flexible data model
  • 7
    Mature
  • 6
    Embeddable
  • 5
    Easy to Use and Model
  • 4
    Best Graphdb
  • 4
    Highly-available
  • 2
    It's awesome, I wanted to try it
  • 2
    Great onboarding process
  • 2
    Great query language and built in data browser
  • 2
    Used by Crunchbase
CONS OF NEO4J
  • 4
    Comparably slow
  • 4
    Can't store a vertex as JSON
  • 1
    Doesn't have a managed cloud service at low cost

related Neo4j posts

We have an in-house build experiment management system. We produce samples as input to the next step, which then could produce 1 sample(1-1) and many samples (1 - many). There are many steps like this. So far, we are tracking genealogy (limited tracking) in the MySQL database, which is becoming hard to trace back to the original material or sample(I can give more details if required). So, we are considering a Graph database. I am requesting advice from the experts.

  1. Is a graph database the right choice, or can we manage with RDBMS?
  2. If RDBMS, which RDMS, which feature, or which approach could make this manageable or sustainable
  3. If Graph database(Neo4j, OrientDB, Azure Cosmos DB, Amazon Neptune, ArangoDB), which one is good, and what are the best practices?

I am sorry that this might be a loaded question.

See more

I'm evaluating the use of RedisGraph vs Microsoft SQL Server 2019 graph features to build a social graph. One of the key criteria is high availability and cross data center replication of data. While Neo4j is a much-matured solution in general, I'm not accounting for it due to the cost & introduction of a new stack in the ecosystem. Also, due to the nature of data & org policies, using a cloud-based solution won't be a viable choice.

We currently use Redis as a cache & SQL server 2019 as RDBMS.

I'm inclining towards SQL server 2019 graph as we already use SQL server extensively as relational database & have all the HA and cross data center replication setup readily available. I still need to evaluate if it fulfills our need as a graph DB though, I also learned that SQL server 2019 is still a new player in the market and attempts to fit a graph-like query on top of a relational model (with node and edge tables). RedisGraph seems very promising. However, I'm not totally sure about HA, Graph data backup, cross-data center support.

See more
Dgraph logo

Dgraph

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167
8
Fast, Distributed Graph DB
98
167
+ 1
8
PROS OF DGRAPH
  • 3
    Graphql as a query language is nice if you like apollo
  • 2
    Low learning curve
  • 1
    High Performance
  • 1
    Open Source
  • 1
    Easy set up
CONS OF DGRAPH
    Be the first to leave a con

    related Dgraph posts

    Saturn logo

    Saturn

    5
    5
    0
    Opinionated, web development framework for F# which implements the server-side, functional MVC pattern
    5
    5
    + 1
    0
    PROS OF SATURN
      Be the first to leave a pro
      CONS OF SATURN
        Be the first to leave a con

        related Saturn posts

        TensorFlow logo

        TensorFlow

        2.7K
        2.9K
        77
        Open Source Software Library for Machine Intelligence
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        2.9K
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        77
        PROS OF TENSORFLOW
        • 25
          High Performance
        • 16
          Connect Research and Production
        • 13
          Deep Flexibility
        • 9
          True Portability
        • 9
          Auto-Differentiation
        • 2
          Easy to use
        • 2
          High level abstraction
        • 1
          Powerful
        CONS OF TENSORFLOW
        • 9
          Hard
        • 6
          Hard to debug
        • 1
          Documentation not very helpful

        related TensorFlow posts

        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.3M views

        Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

        At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

        TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

        Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

        https://eng.uber.com/horovod/

        (Direct GitHub repo: https://github.com/uber/horovod)

        See more

        In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

        Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

        !

        See more
        PyTorch logo

        PyTorch

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        A deep learning framework that puts Python first
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        1.1K
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        42
        PROS OF PYTORCH
        • 14
          Easy to use
        • 11
          Developer Friendly
        • 10
          Easy to debug
        • 7
          Sometimes faster than TensorFlow
        CONS OF PYTORCH
        • 3
          Lots of code

        related PyTorch posts

        Server side

        We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

        • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

        • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

        • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

        Client side

        • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

        • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

        • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

        Cache

        • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

        Database

        • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

        Infrastructure

        • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

        Other Tools

        • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

        • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

        See more
        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.3M views

        Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

        At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

        TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

        Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

        https://eng.uber.com/horovod/

        (Direct GitHub repo: https://github.com/uber/horovod)

        See more
        Keras logo

        Keras

        926
        960
        14
        Deep Learning library for Theano and TensorFlow
        926
        960
        + 1
        14
        PROS OF KERAS
        • 5
          Easy and fast NN prototyping
        • 5
          Quality Documentation
        • 4
          Supports Tensorflow and Theano backends
        CONS OF KERAS
        • 3
          Hard to debug

        related Keras posts

        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.3M views

        Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

        At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

        TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

        Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

        https://eng.uber.com/horovod/

        (Direct GitHub repo: https://github.com/uber/horovod)

        See more

        I am going to send my website to a Venture Capitalist for inspection. If I succeed, I will get funding for my StartUp! This website is based on Django and Uses Keras and TensorFlow model to predict medical imaging. Should I use Heroku or PythonAnywhere to deploy my website ?? Best Regards, Adarsh.

        See more
        scikit-learn logo

        scikit-learn

        900
        943
        36
        Easy-to-use and general-purpose machine learning in Python
        900
        943
        + 1
        36
        PROS OF SCIKIT-LEARN
        • 20
          Scientific computing
        • 16
          Easy
        CONS OF SCIKIT-LEARN
        • 1
          Limited

        related scikit-learn posts

        CUDA logo

        CUDA

        191
        127
        0
        It provides everything you need to develop GPU-accelerated applications
        191
        127
        + 1
        0
        PROS OF CUDA
          Be the first to leave a pro
          CONS OF CUDA
            Be the first to leave a con

            related CUDA posts