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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. Kubeflow vs Neptune

Kubeflow vs Neptune

OverviewComparisonAlternatives

Overview

Kubeflow
Kubeflow
Stacks205
Followers585
Votes18
Neptune
Neptune
Stacks16
Followers38
Votes2

Kubeflow vs Neptune: What are the differences?

Developers describe Kubeflow as "Machine Learning Toolkit for Kubernetes". The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions. On the other hand, Neptune is detailed as "The most lightweight experiment tracking tool for machine learning". 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.

Kubeflow and Neptune can be primarily classified as "Machine Learning" tools.

Kubeflow is an open source tool with 8.31K GitHub stars and 1.3K GitHub forks. Here's a link to Kubeflow's open source repository on GitHub.

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Detailed Comparison

Kubeflow
Kubeflow
Neptune
Neptune

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

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.

-
Experiment tracking; Experiment versioning; Experiment comparison; Experiment monitoring; Experiment sharing; Notebook versioning
Statistics
Stacks
205
Stacks
16
Followers
585
Followers
38
Votes
18
Votes
2
Pros & Cons
Pros
  • 9
    System designer
  • 3
    Customisation
  • 3
    Kfp dsl
  • 3
    Google backed
  • 0
    Azure
Pros
  • 1
    Aws managed services
  • 1
    Supports both gremlin and openCypher query languages
Cons
  • 1
    Doesn't have much support for openCypher clients
  • 1
    Doesn't have much community support
  • 1
    Doesn't have proper clients for different lanuages
Integrations
Kubernetes
Kubernetes
Jupyter
Jupyter
TensorFlow
TensorFlow
PyTorch
PyTorch
Keras
Keras
R Language
R Language
MLflow
MLflow
Matplotlib
Matplotlib

What are some alternatives to Kubeflow, Neptune?

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.

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.

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/

TensorFlow.js

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

PredictionIO

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

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