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  1. Stackups
  2. DevOps
  3. Build Automation
  4. Python Build Tools
  5. Neptune vs XGBoost

Neptune vs XGBoost

OverviewComparisonAlternatives

Overview

XGBoost
XGBoost
Stacks192
Followers86
Votes0
GitHub Stars27.6K
Forks8.8K
Neptune
Neptune
Stacks16
Followers38
Votes2

XGBoost vs Neptune: What are the differences?

What is XGBoost? Scalable and Flexible Gradient Boosting. Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow.

What is Neptune? 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.

XGBoost belongs to "Python Build Tools" category of the tech stack, while Neptune can be primarily classified under "Machine Learning Tools".

Some of the features offered by XGBoost are:

  • Flexible
  • Portable
  • Multiple Languages

On the other hand, Neptune provides the following key features:

  • Experiment tracking
  • Experiment versioning
  • Experiment comparison

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Detailed Comparison

XGBoost
XGBoost
Neptune
Neptune

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow

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.

Flexible; Portable; Multiple Languages; Battle-tested
Experiment tracking; Experiment versioning; Experiment comparison; Experiment monitoring; Experiment sharing; Notebook versioning
Statistics
GitHub Stars
27.6K
GitHub Stars
-
GitHub Forks
8.8K
GitHub Forks
-
Stacks
192
Stacks
16
Followers
86
Followers
38
Votes
0
Votes
2
Pros & Cons
No community feedback yet
Pros
  • 1
    Supports both gremlin and openCypher query languages
  • 1
    Aws managed services
Cons
  • 1
    Doesn't have much community support
  • 1
    Doesn't have proper clients for different lanuages
  • 1
    Doesn't have much support for openCypher clients
Integrations
Python
Python
C++
C++
Java
Java
Scala
Scala
Julia
Julia
PyTorch
PyTorch
Keras
Keras
R Language
R Language
MLflow
MLflow
Matplotlib
Matplotlib

What are some alternatives to XGBoost, 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/

Kubeflow

Kubeflow

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.

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.

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