Kubeflow vs XGBoost

Overview

Kubeflow

Stacks205

Followers585

Votes18

XGBoost

Stacks192

Followers86

Votes0

GitHub Stars27.6K

Forks8.8K

Kubeflow vs XGBoost: What are the differences?

Kubeflow: 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; 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.

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

Kubeflow is an open source tool with 7.26K GitHub stars and 1.08K GitHub forks. Here's a link to Kubeflow's open source repository on GitHub.

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Detailed Comparison

Kubeflow	XGBoost
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.	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
-	Flexible; Portable; Multiple Languages; Battle-tested
Statistics
GitHub Stars -	GitHub Stars 27.6K
GitHub Forks -	GitHub Forks 8.8K
Stacks 205	Stacks 192
Followers 585	Followers 86
Votes 18	Votes 0
Pros & Cons
Pros 9 System designer 3 Customisation 3 Kfp dsl 3 Google backed 0 Azure	No community feedback yet
Integrations
Kubernetes Jupyter TensorFlow	Python C++ Java Scala Julia

What are some alternatives to Kubeflow, XGBoost?

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

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

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

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

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

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

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